nn-to-predict-cardiovascular-diseases.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NEURAL NETWORK TO PREDICT CARDIOVASCULAR DISEASES"
   ]
  },

  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['weights-nn', 'cardiovascular-disease-dataset']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "import seaborn as sns             # visualizations\n",
    "import matplotlib.pyplot as plt   # visualizations\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import np_utils\n",
    "import os\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18393</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>62.0</td>\n",
       "      <td>110</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20228</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>85.0</td>\n",
       "      <td>140</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18857</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>130</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17623</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>82.0</td>\n",
       "      <td>150</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17474</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>56.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  gender  height  weight   ...    smoke  alco  active  cardio\n",
       "id                                  ...                               \n",
       "0   18393       2     168    62.0   ...        0     0       1       0\n",
       "1   20228       1     156    85.0   ...        0     0       1       1\n",
       "2   18857       1     165    64.0   ...        0     0       0       1\n",
       "3   17623       2     169    82.0   ...        0     0       1       1\n",
       "4   17474       1     156    56.0   ...        0     0       0       0\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"../input/cardiovascular-disease-dataset/cardio_train.csv\")\n",
    "df=data.from_csv(\"../input/cardiovascular-disease-dataset/cardio_train.csv\", header=0, sep=\";\")\n",
    "dfcol=df.columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking if the targets are balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcef203feb8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"cardio\",\"height\"]].groupby(\"cardio\").count()\n",
    "sns.countplot(x=\"cardio\", data=df, palette=\"Set1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Transform (Scaling to avoid outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.588076</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.579487</td>\n",
       "      <td>0.273684</td>\n",
       "      <td>0.016079</td>\n",
       "      <td>0.013550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.730159</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.517949</td>\n",
       "      <td>0.394737</td>\n",
       "      <td>0.017934</td>\n",
       "      <td>0.014453</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.624003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>0.284211</td>\n",
       "      <td>0.017316</td>\n",
       "      <td>0.012647</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.528455</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.584615</td>\n",
       "      <td>0.378947</td>\n",
       "      <td>0.018553</td>\n",
       "      <td>0.015357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.516918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.517949</td>\n",
       "      <td>0.242105</td>\n",
       "      <td>0.015461</td>\n",
       "      <td>0.011743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  gender    height    weight   ...    smoke  alco  active  cardio\n",
       "0  0.588076     1.0  0.579487  0.273684   ...      0.0   0.0     1.0     0.0\n",
       "1  0.730159     0.0  0.517949  0.394737   ...      0.0   0.0     1.0     1.0\n",
       "2  0.624003     0.0  0.564103  0.284211   ...      0.0   0.0     0.0     1.0\n",
       "3  0.528455     1.0  0.584615  0.378947   ...      0.0   0.0     1.0     1.0\n",
       "4  0.516918     0.0  0.517949  0.242105   ...      0.0   0.0     0.0     0.0\n",
       "\n",
       "[5 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn import preprocessing\n",
    "scaler=preprocessing.MinMaxScaler()\n",
    "dfscale=scaler.fit_transform(df)\n",
    "dfscale2=pd.DataFrame(dfscale, columns=dfcol)\n",
    "dfscale2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xdf=dfscale2.iloc[:,0:11]\n",
    "#xdf[\"gender\"]=np.where(xdf[\"gender\"]==1,\"0\",\"1\") #Cambiar el 2 por 1, el 1 por 0 (por orden)\n",
    "#Aca vendria un posible drop de variables xdf=xdf.drop([\"gender\",\"gluc\"], axis=1)\n",
    "ydf=dfscale2.iloc[:,-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_training, x_testing, y_training, y_testing = train_test_split(xdf, ydf, test_size = 0.2, random_state=123, stratify=ydf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 11)\n"
     ]
    }
   ],
   "source": [
    "print(xdf.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 25)                300       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 5)                 130       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 3)                 18        \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 4         \n",
      "=================================================================\n",
      "Total params: 452\n",
      "Trainable params: 452\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Dropout\n",
    "from keras.constraints import maxnorm\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(25, input_dim=11, activation='softsign', kernel_constraint=maxnorm(2)))\n",
    "#model.add(Dropout(0))\n",
    "model.add(Dense(5, activation='softsign'))\n",
    "#model.add(Dropout(0))\n",
    "model.add(Dense(3, activation='softsign'))\n",
    "#model.add(Dropout(0))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss = 'binary_crossentropy', optimizer='Nadam', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56000/56000 [==============================] - 1s 18us/step\n",
      "\n",
      " Training Accuracy: 0.7065178571428572\n",
      "14000/14000 [==============================] - 0s 17us/step\n",
      "\n",
      " Testing Accuracy: 0.7124285714285714\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_training, y_training, epochs=50, batch_size=50, verbose=0)\n",
    "score = model.evaluate(x_training, y_training)\n",
    "print(\"\\n Training Accuracy:\", score[1])\n",
    "score = model.evaluate(x_testing, y_testing)\n",
    "print(\"\\n Testing Accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Initial Accuracy**= 0.6465 \n",
    "* **Final aprox Accuracy**= 0.68-0.719"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcef142d748>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res=model.predict(x_testing)\n",
    "res\n",
    "resdf=pd.DataFrame(res, index=x_testing.index)\n",
    "resdf.columns=[\"Pr\"]\n",
    "resdf[\"ID\"]=range(14000)\n",
    "resdf[\"y\"]=np.where(resdf[\"Pr\"]>=0.5,\"1\", \"0\")\n",
    "resdf\n",
    "prediction=resdf.drop([\"Pr\",\"ID\"], axis=1)\n",
    "predictionarray=prediction.astype(np.float)\n",
    "sns.distplot(resdf[\"Pr\"],  color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many 1's and 0's predict the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID\n",
       "y      \n",
       "0  6766\n",
       "1  7234"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1=resdf[['ID','y']].groupby('y').count()\n",
    "c1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many 1's and 0's are in the test sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardio</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>7004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID\n",
       "cardio      \n",
       "0.0     7004\n",
       "1.0     6996"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_testingdf=pd.DataFrame(y_testing, index=y_testing.index)\n",
    "y_testingdf[\"ID\"]=range(14000)\n",
    "y_test=y_testingdf.drop([\"ID\"], axis=1)\n",
    "c2=y_testingdf[['ID','cardio']].groupby('cardio').count()\n",
    "c2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4872, 2132],\n",
       "       [1894, 5102]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(y_test.values, predictionarray)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accuracy of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is: 72.00709429500444 %\n"
     ]
    }
   ],
   "source": [
    "Accuracy=cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print(\"The accuracy of the model is: \"+ str(Accuracy*100) + \" %\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that of all the people with Cardiovascular Disease in the Test-DataBase, the model identify **73% (aprox.)** of the total cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting a single case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.248471</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.65641</td>\n",
       "      <td>0.415789</td>\n",
       "      <td>0.016698</td>\n",
       "      <td>0.01355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0    1        2         3         4  ...    6    7    8    9    10\n",
       "0 -0.248471  1.0  0.65641  0.415789  0.016698 ...   0.0  0.0  0.0  0.0  1.0\n",
       "\n",
       "[1 rows x 11 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#INSERT DATA#\n",
    "###############################################################################\n",
    "\n",
    "day= 25 # day of bith \n",
    "month= 9 # month of bith (in numbers)\n",
    "year= 1998 # year of bith\n",
    "gender= 1 # 0 for women, 1 for men\n",
    "height= 183 # in cm\n",
    "weight= 89 # in kilograms\n",
    "systolicbloodpressure= 120 # Systolic blood pressure\n",
    "diastolicbloodpressure= 80 # Diastolic blood pressure\n",
    "cholesterol= 1 # 1: normal, 2: above normal, 3: well above normal\n",
    "gluc= 1 # 1: normal, 2: above normal, 3: well above normal\n",
    "smoke= 0 # 1 if you smoke, 0 if not\n",
    "alco= 0 # 1 if you drink alcohol, 0 if not\n",
    "active= 1 # 1 if you do physical activity, 0 if not\n",
    "\n",
    "##############################################################################\n",
    "from datetime import date\n",
    "f_date = date(year,month,day)\n",
    "l_date = date.today()\n",
    "delta = l_date - f_date\n",
    "agedays=delta.days\n",
    "\n",
    "agedayscale=(agedays-df[\"age\"].min())/(df[\"age\"].max()-df[\"age\"].min())\n",
    "heightscale=(height-df[\"height\"].min())/(df[\"height\"].max()-df[\"height\"].min())\n",
    "weightscale=(weight-df[\"weight\"].min())/(df[\"weight\"].max()-df[\"weight\"].min())\n",
    "sbpscale=(systolicbloodpressure-df[\"ap_hi\"].min())/(df[\"ap_hi\"].max()-df[\"ap_hi\"].min())\n",
    "dbpscale=(diastolicbloodpressure-df[\"ap_lo\"].min())/(df[\"ap_lo\"].max()-df[\"ap_lo\"].min())\n",
    "cholesterolscale=(cholesterol-df[\"cholesterol\"].min())/(df[\"cholesterol\"].max()-df[\"cholesterol\"].min())\n",
    "glucscale=(gluc-df[\"gluc\"].min())/(df[\"gluc\"].max()-df[\"gluc\"].min())\n",
    "\n",
    "single=np.array([agedayscale, gender, heightscale, weightscale, sbpscale, dbpscale, cholesterolscale, glucscale, smoke, alco, active ])\n",
    "singledf=pd.DataFrame(single)\n",
    "final=singledf.transpose()\n",
    "final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "finalres=model.predict(final)\n",
    "finalres\n",
    "print(\"The probability of having or to have a Cardiovascular Disease is: \"+ str(round(finalres[0,0]*100,2)) + \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of having or to have a Cardiovascular Disease is: 14.02%\n",
      "You are healthy :) \n"
     ]
    }
   ],
   "source": [
    "from keras.models import model_from_json\n",
    "\n",
    "#model.save_weights(\"weights.hdf5\")\n",
    "json_string = model.to_json()\n",
    "modeltopredict = model_from_json(json_string)\n",
    "modeltopredict.load_weights(\"../input/weights-nn/weights.hdf5\", by_name=False)\n",
    "\n",
    "prediction=modeltopredict.predict(final)\n",
    "\n",
    "if prediction[0,0]>=0.5:\n",
    "    print(\"The probability of having or to have a Cardiovascular Disease is: \"+ str(round(prediction[0,0]*100,2)) + \"%\")\n",
    "    print(\"You must visit a doctor to check it :(\")\n",
    "elif prediction[0,0]<0.5 and prediction[0,0]>=0.3:\n",
    "    print(\"The probability of having or to have a Cardiovascular Disease is: \"+ str(round(prediction[0,0]*100,2)) + \"%\")\n",
    "    print(\"Probably you are healthy :/ \")\n",
    "else:\n",
    "    print(\"The probability of having or to have a Cardiovascular Disease is: \"+ str(round(prediction[0,0]*100,2)) + \"%\")\n",
    "    print(\"You are healthy :) \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning hyperparameters to look up for the best NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Size and Epochs\n",
    "\n",
    "In the final kernel, this code is not gonna be available 'cause it takes too much time to run this parts of code.\n",
    "The summary will be written below the function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def create_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=11, activation='tanh'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(5, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(3, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "batch_size = [10, 50, 100]\n",
    "epochs = [10, 50, 100]\n",
    "param_grid = dict(batch_size=batch_size, epochs=epochs)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:** \n",
    "* Best: 0.640893 using {**'batch_size': 50, 'epochs': 50**}\n",
    "* 0.638214 (0.003997) with: {'batch_size': 10, 'epochs': 10}\n",
    "* 0.640446 (0.006447) with: {'batch_size': 10, 'epochs': 50}\n",
    "* 0.613500 (0.060568) with: {'batch_size': 10, 'epochs': 100}\n",
    "* 0.617089 (0.012489) with: {'batch_size': 50, 'epochs': 10}\n",
    "* 0.640893 (0.004402) with: {'batch_size': 50, 'epochs': 50}\n",
    "* 0.640661 (0.003730) with: {'batch_size': 50, 'epochs': 100}\n",
    "* 0.613732 (0.017299) with: {'batch_size': 100, 'epochs': 10}\n",
    "* 0.635268 (0.005980) with: {'batch_size': 100, 'epochs': 50}\n",
    "* 0.639482 (0.007552) with: {'batch_size': 100, 'epochs': 100}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization Algorithm\n",
    "\n",
    "In the final kernel, this code is not gonna be available 'cause it takes too much time to run this parts of code.\n",
    "The summary will be written below the function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### function to create model\n",
    "def create_model(optimizer='adam'):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=11, activation='tanh'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(5, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(3, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=50, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']\n",
    "param_grid = dict(optimizer=optimizer)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:** \n",
    "* Best: 0.708232 using {**'optimizer': 'Nadam'**}\n",
    "* 0.639339 (0.003833) with: {'optimizer': 'SGD'}\n",
    "* 0.685446 (0.014199) with: {'optimizer': 'RMSprop'}\n",
    "* 0.644750 (0.004682) with: {'optimizer': 'Adagrad'}\n",
    "* 0.664732 (0.011830) with: {'optimizer': 'Adadelta'}\n",
    "* 0.676821 (0.017248) with: {'optimizer': 'Adam'}\n",
    "* 0.653625 (0.003463) with: {'optimizer': 'Adamax'}\n",
    "* 0.708232 (0.010960) with: {'optimizer': 'Nadam'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization of SGD algorithm (Learnig Rate and Momentum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### function to create model\n",
    "def create_model(learn_rate=0.01, momentum=0):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=11, activation='tanh'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(5, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(3, activation='relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    optimizer = SGD(lr=learn_rate, momentum=momentum)\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=50, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "learn_rate = [0.01, 0.1, 0.2]\n",
    "momentum = [0.2, 0.6, 0.9]\n",
    "param_grid = dict(learn_rate=learn_rate, momentum=momentum)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:** \n",
    "* Best: 0.642571 using {**'learn_rate': 0.01, 'momentum': 0.2**}\n",
    "* 0.642571 (0.004326) with: {'learn_rate': 0.01, 'momentum': 0.2}\n",
    "* 0.642375 (0.003565) with: {'learn_rate': 0.01, 'momentum': 0.6}\n",
    "* 0.641786 (0.005498) with: {'learn_rate': 0.01, 'momentum': 0.9}\n",
    "* 0.586232 (0.069370) with: {'learn_rate': 0.1, 'momentum': 0.2}\n",
    "* 0.642089 (0.004087) with: {'learn_rate': 0.1, 'momentum': 0.6}\n",
    "* 0.638589 (0.006434) with: {'learn_rate': 0.1, 'momentum': 0.9}\n",
    "* 0.498554 (0.005800) with: {'learn_rate': 0.2, 'momentum': 0.2}\n",
    "* 0.587482 (0.071161) with: {'learn_rate': 0.2, 'momentum': 0.6}\n",
    "* 0.594286 (0.058599) with: {'learn_rate': 0.2, 'momentum': 0.9}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def create_model(activation='relu'):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=11, activation=activation))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(5, activation=activation))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(3, activation=activation))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer=\"Nadam\", metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=50, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "activation = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']\n",
    "param_grid = dict(activation=activation)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:** \n",
    "* Best: 0.717250 using {**'activation': 'softsign'**}\n",
    "* 0.649786 (0.005472) with: {'activation': 'softmax'}\n",
    "* 0.649464 (0.005333) with: {'activation': 'softplus'}\n",
    "* 0.717250 (0.006675) with: {'activation': 'softsign'}\n",
    "* 0.701964 (0.021487) with: {'activation': 'relu'}\n",
    "* 0.717179 (0.002289) with: {'activation': 'tanh'}\n",
    "* 0.648446 (0.006001) with: {'activation': 'sigmoid'}\n",
    "* 0.651750 (0.004286) with: {'activation': 'hard_sigmoid'}\n",
    "* 0.709232 (0.015933) with: {'activation': 'linear'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropout Rate and Weight Constraint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from keras.layers import Dropout\n",
    "from keras.constraints import maxnorm\n",
    "\n",
    "#### function to create model\n",
    "def create_model(dropout_rate=0.0, weight_constraint=0):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(10, input_dim=11, activation='softsign', kernel_constraint=maxnorm(weight_constraint)))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(5, activation='softsign'))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(3, activation='softsign'))\n",
    "    model.add(Dropout(dropout_rate))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer='Nadam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=50, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "weight_constraint = [0, 1, 2]\n",
    "dropout_rate = [0.0, 0.1, 0.2]\n",
    "param_grid = dict(dropout_rate=dropout_rate, weight_constraint=weight_constraint)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:**\n",
    "* Best: 0.701179 using {**'dropout_rate': 0.0, 'weight_constraint': 2**}\n",
    "* 0.500839 (0.005918) with: {'dropout_rate': 0.0, 'weight_constraint': 0}\n",
    "* 0.651839 (0.005446) with: {'dropout_rate': 0.0, 'weight_constraint': 1}\n",
    "* 0.701179 (0.010650) with: {'dropout_rate': 0.0, 'weight_constraint': 2}\n",
    "* 0.500446 (0.005961) with: {'dropout_rate': 0.1, 'weight_constraint': 0}\n",
    "* 0.648250 (0.007944) with: {'dropout_rate': 0.1, 'weight_constraint': 1}\n",
    "* 0.684536 (0.022927) with: {'dropout_rate': 0.1, 'weight_constraint': 2}\n",
    "* 0.500304 (0.005970) with: {'dropout_rate': 0.2, 'weight_constraint': 0}\n",
    "* 0.649732 (0.002889) with: {'dropout_rate': 0.2, 'weight_constraint': 1}\n",
    "* 0.666893 (0.011692) with: {'dropout_rate': 0.2, 'weight_constraint': 2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of neurons in the first layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "def create_model(neurons=1):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(neurons, input_dim=11, activation='softsign', kernel_constraint=maxnorm(2)))\n",
    "    model.add(Dropout(0))\n",
    "    model.add(Dense(5, activation='softsign'))\n",
    "    model.add(Dropout(0))\n",
    "    model.add(Dense(3, activation='softsign'))\n",
    "    model.add(Dropout(0))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss = 'binary_crossentropy', optimizer='Nadam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "#### create model\n",
    "model = KerasClassifier(build_fn=create_model, epochs=50, batch_size=50, verbose=0)\n",
    "\n",
    "#### define the parameters to search in grid search \n",
    "neurons = [1, 5, 10, 15, 20, 25, 30]\n",
    "param_grid = dict(neurons=neurons)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=5)\n",
    "grid_result = grid.fit(x_training, y_training)\n",
    "\n",
    "#### summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results:**\n",
    "* Best: 0.703107 using {**'neurons': 25**}\n",
    "* 0.683607 (0.003922) with: {'neurons': 1}\n",
    "* 0.700411 (0.003911) with: {'neurons': 5}\n",
    "* 0.694625 (0.009716) with: {'neurons': 10}\n",
    "* 0.699536 (0.009807) with: {'neurons': 15}\n",
    "* 0.698893 (0.013422) with: {'neurons': 20}\n",
    "* 0.703107 (0.014787) with: {'neurons': 25}\n",
    "* 0.688821 (0.026863) with: {'neurons': 30}"
   ]
  }
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