heart_desease_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "heart_desease_classification.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iqbalhanif/NN-with-Keras/blob/master/heart_desease_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lac6_xZ3wTwo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "985b2c56-1345-43d5-d0d3-22c015929f25"
      },
      "source": [
        "# Get Data\n",
        "!wget https://raw.githubusercontent.com/trojrobert/Classification-of-heart-disease-uci-data-/master/heart.csv"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-09-20 05:08:27--  https://raw.githubusercontent.com/trojrobert/Classification-of-heart-disease-uci-data-/master/heart.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11024 (11K) [text/plain]\n",
            "Saving to: ‘heart.csv’\n",
            "\n",
            "heart.csv           100%[===================>]  10.77K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-09-20 05:08:27 (78.0 MB/s) - ‘heart.csv’ saved [11024/11024]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GJQCjM8yPBy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "bb39671d-2f52-49e0-e94f-ac54bc8ae787"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"heart.csv\")\n",
        "df.head(4)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "\n",
              "[4 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuJx0c1fzOL3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "e6d31752-8955-4e55-bd03-9b03450a3bf1"
      },
      "source": [
        "df2 = df[df['age'] > 50]\n",
        "df3 = df[(df['age'] > 50)&(df['chol']>200)]\n",
        "df3\n",
        "#print(\"Length Ori: \"+ str(len(df)))\n",
        "#print(\"Length Filter: \"+ str(len(df2)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>294</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>153</td>\n",
              "      <td>0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>291</th>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>114</td>\n",
              "      <td>318</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>225</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>67</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>152</td>\n",
              "      <td>212</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>241</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>123</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>181 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "6     56    0   1       140   294    0  ...      0      1.3      1   0     2       1\n",
              "10    54    1   0       140   239    0  ...      0      1.2      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "291   58    1   0       114   318    0  ...      0      4.4      0   3     1       0\n",
              "292   58    0   0       170   225    1  ...      1      2.8      1   2     1       0\n",
              "293   67    1   2       152   212    0  ...      0      0.8      1   0     3       0\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[181 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MDzx4G8y0_A4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83
        },
        "outputId": "317feb56-1ba6-4ba4-d59a-245853fb7d2b"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df.iloc[:, 0:13]\n",
        "y = df.iloc[:, 13]\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
        "print(\"jml xtrain = \"+str(len(X_train)))\n",
        "print(\"jml xtest = \"+str(len(X_test)))\n",
        "print(\"jml ytrain = \"+str(len(y_train)))\n",
        "print(\"jml ytest = \"+str(len(y_test)))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "jml xtrain = 212\n",
            "jml xtest = 91\n",
            "jml ytrain = 212\n",
            "jml ytest = 91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mv9IpfIF5dgv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a79a5aa2-4e3c-4a76-a22e-1e02d523d81b"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(12, input_dim=13, activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
        "model.fit(X_train, y_train, epochs=300, batch_size=10, verbose=0)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd2a1247a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BD03_mbg6qjU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "64b8fa10-ce88-401a-d731-3f233e32aabb"
      },
      "source": [
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "y_pred_model = model.predict_classes(X_test)\n",
        "print(classification_report(y_pred_model, y_test))\n",
        "print(\"Akurasi: \"+ str(accuracy_score(y_pred_model, y_test)))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-10-9799a9ba16b5>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.64      0.73        56\n",
            "           1       0.58      0.80      0.67        35\n",
            "\n",
            "    accuracy                           0.70        91\n",
            "   macro avg       0.71      0.72      0.70        91\n",
            "weighted avg       0.74      0.70      0.71        91\n",
            "\n",
            "Akurasi: 0.7032967032967034\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDXln6Ey9Ks2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c71bba51-d60a-4e22-9ad9-9daf724a1903"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = model.predict_classes(X_test)\n",
        "print(confusion_matrix(y_pred, y_test))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[36 20]\n",
            " [ 7 28]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}