Heart_Attack_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled_(6).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.1"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "EgZ2rKzrqDkx"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn import tree\n",
        "from sklearn import svm,naive_bayes\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from pandas import read_csv\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "lrUhHy57qDk-",
        "outputId": "bd2ec767-d01d-4d37-cb70-750e8572c824"
      },
      "source": [
        "df=pd.read_csv('Blood_Pressure_data.csv')\n",
        "g = df[\"label\"]\n",
        "sns.countplot(g)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d0f3eb470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "vBUhAW6oqDk_",
        "outputId": "c2714044-d406-479c-ec40-e8438b8b28f9"
      },
      "source": [
        "\n",
        "# df=df.drop(\"id\",axis=1)\n",
        "# df=df.drop(\"patient_no\",axis=1)\n",
        "df=df.drop(\"cast\",axis=1)\n",
        "# df=df.drop(\"gender\",axis=1)\n",
        "df=df.drop(\"weight\",axis=1)\n",
        "# df=df.drop(\"admission_typeid\",axis=1)\n",
        "# df=df.drop(\"discharge_disposition_id\",axis=1)\n",
        "# df=df.drop(\"time_in_hospital\",axis=1)\n",
        "# df=df.drop(\"admission_source_id\",axis=1)\n",
        "df=df.drop(\"payer_code\",axis=1)\n",
        "# df=df.drop(\"num_lab_procedures\",axis=1)\n",
        "# df=df.drop(\"num_procedures\",axis=1)\n",
        "df=df.drop(\"medical_specialty\",axis=1)\n",
        "df=df.drop(\"diag_3\",axis=1)\n",
        "df=df.drop(\"diag_2\",axis=1)\n",
        "df=df.drop(\"diag_1\",axis=1)\n",
        "df=df.drop(\"change\",axis=1)\n",
        "df=df.drop(\"Med\",axis=1)\n",
        "df.head()"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>patient_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>age group</th>\n",
              "      <th>admission_typeid</th>\n",
              "      <th>discharge_disposition_id</th>\n",
              "      <th>admission_source_id</th>\n",
              "      <th>time_in_hospital</th>\n",
              "      <th>num_lab_procedures</th>\n",
              "      <th>num_procedures</th>\n",
              "      <th>num_medications</th>\n",
              "      <th>number_outpatient</th>\n",
              "      <th>number_emergency</th>\n",
              "      <th>number_inpatient</th>\n",
              "      <th>number_diagnoses</th>\n",
              "      <th>max_glu_serum</th>\n",
              "      <th>A1Cresult</th>\n",
              "      <th>metformin</th>\n",
              "      <th>repaglinide</th>\n",
              "      <th>nateglinide</th>\n",
              "      <th>chlorpropamide</th>\n",
              "      <th>glimepiride</th>\n",
              "      <th>acetohexamide</th>\n",
              "      <th>glipizide</th>\n",
              "      <th>glyburide</th>\n",
              "      <th>tolbutamide</th>\n",
              "      <th>pioglitazone</th>\n",
              "      <th>rosiglitazone</th>\n",
              "      <th>acarbose</th>\n",
              "      <th>miglitol</th>\n",
              "      <th>troglitazone</th>\n",
              "      <th>tolazamide</th>\n",
              "      <th>examide</th>\n",
              "      <th>citoglipton</th>\n",
              "      <th>insulin</th>\n",
              "      <th>glyburide-metformin</th>\n",
              "      <th>glipizide-metformin</th>\n",
              "      <th>glimepiride-pioglitazone</th>\n",
              "      <th>metformin-rosiglitazone</th>\n",
              "      <th>metformin-pioglitazone</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2278392</td>\n",
              "      <td>8222157</td>\n",
              "      <td>Female</td>\n",
              "      <td>[0-10)</td>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>149190</td>\n",
              "      <td>55629189</td>\n",
              "      <td>Female</td>\n",
              "      <td>[10-20)</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>59</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Up</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>&gt;5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>64410</td>\n",
              "      <td>86047875</td>\n",
              "      <td>Female</td>\n",
              "      <td>[20-30)</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Steady</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>500364</td>\n",
              "      <td>82442376</td>\n",
              "      <td>Male</td>\n",
              "      <td>[30-40)</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Up</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16680</td>\n",
              "      <td>42519267</td>\n",
              "      <td>Male</td>\n",
              "      <td>[40-50)</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Steady</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Steady</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>NO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id  patient_no  ... metformin-pioglitazone label\n",
              "0  2278392     8222157  ...                     No    NO\n",
              "1   149190    55629189  ...                     No    >5\n",
              "2    64410    86047875  ...                     No    NO\n",
              "3   500364    82442376  ...                     No    NO\n",
              "4    16680    42519267  ...                     No    NO\n",
              "\n",
              "[5 rows x 41 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "0HeLObwbqDlA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1ebaccc-78b1-4668-9720-a3a347d544c9"
      },
      "source": [
        "df_copy=df.copy()\n",
        "df_copy=df_copy.replace('?', np.NaN)\n",
        "# df_copy[\"payer_code\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"payer_code\"].fillna('MD',inplace = True)\n",
        "# df_copy[\"weight\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"weight\"].fillna('[25-50)',inplace = True)\n",
        "# df_copy[\"cast\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"medical_specialty\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"medical_specialty\"].fillna('Urology',inplace = True)\n",
        "# df_copy[\"diag_1\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"diag_2\"].fillna( method ='backfill',inplace = True)\n",
        "# df_copy[\"diag_3\"].fillna( method ='backfill',inplace = True)\n",
        "df_copy.fillna(method ='ffill',inplace = True)\n",
        "df_copy.isnull().sum().sum()"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ja3XHSZujzJ"
      },
      "source": [
        "data = df_copy.values\r\n",
        "X = df_copy.drop(\"label\", axis = 1)\r\n",
        "d={'NO':0,'>5':1, '<30':2}\r\n",
        "df_copy[\"label\"]=df_copy[\"label\"].map(d)\r\n",
        "y = df_copy[\"label\"]\r\n",
        "ordinal_encoder = OrdinalEncoder()\r\n",
        "X = ordinal_encoder.fit_transform(X)\r\n",
        "label_encoder = LabelEncoder()"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWro2J2FqDlB"
      },
      "source": [
        "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.2, random_state=51)\n",
        "sc = StandardScaler()\n",
        "sc.fit(X_train)\n",
        "X_train_sc = sc.transform(X_train)\n",
        "X_test_sc = sc.transform(X_test)\n",
        "X_train_sc =  pd.DataFrame(X_train_sc)\n",
        "X_test_sc =  pd.DataFrame(X_test_sc)"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5076z_YvMpL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c622b1bb-ee61-4428-df15-2d11af2bd922"
      },
      "source": [
        "# decision tree\r\n",
        "clf = tree.DecisionTreeClassifier(random_state=0,max_depth=9)\r\n",
        "clf = clf.fit(X_train, y_train)\r\n",
        "pred=clf.predict(X_test)\r\n",
        "score_dt = round(accuracy_score(pred,y_test)*100,2)\r\n",
        "print(score_dt)\r\n",
        "f1 = f1_score(y_test,pred,average='macro')\r\n",
        "print(f1)"
      ],
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "59.16\n",
            "0.4235629223942932\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i_BL0VBPYIX5",
        "outputId": "f9ed876e-d5f9-46e6-8b42-3a78fe066be3"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\r\n",
        "nb = GaussianNB()\r\n",
        "nb.fit(X_train,y_train)\r\n",
        "Y_pred_nb = nb.predict(X_test)\r\n",
        "score_nb = round(accuracy_score(Y_pred_nb,y_test)*100,2)\r\n",
        "nb.score(X_test_sc,y_test)\r\n",
        "print(score_nb)\r\n",
        "f1 = f1_score(y_test,Y_pred_nb,average='macro')\r\n",
        "print(f1)"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "55.79\n",
            "0.34580665907988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkT-UZoFcLlO"
      },
      "source": [
        "# from sklearn.linear_model import LogisticRegression\r\n",
        "# lr = LogisticRegression()\r\n",
        "# lr.fit(X_train,y_train)\r\n",
        "# Y_pred_lr = lr.predict(X_test)\r\n",
        "# score_lr = round(accuracy_score(Y_pred_lr,y_test)*100,2)\r\n",
        "# lr.score(X_test_sc,y_test)\r\n",
        "# print(score_lr)\r\n",
        "# f1 = f1_score(y_test,Y_pred_lr,average='macro')\r\n",
        "# print(f1)"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lZ5rTQvbCaA",
        "outputId": "bd929729-c2a9-4357-87d5-03b6104e2a1b"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\r\n",
        "knn = KNeighborsClassifier(n_neighbors=70)\r\n",
        "knn.fit(X_train,y_train)\r\n",
        "Y_pred_knn=knn.predict(X_test)\r\n",
        "score_knn = round(accuracy_score(Y_pred_knn,y_test)*100,2)\r\n",
        "knn.score(X_test_sc,y_test)\r\n",
        "print(score_knn)\r\n",
        "f1 = f1_score(y_test,Y_pred_knn,average='macro')\r\n",
        "print(f1)"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "55.04\n",
            "0.35037866165830506\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exAYlWetnB9p",
        "outputId": "f60b1019-9f9d-49a0-8b36-06e32eb62ce1"
      },
      "source": [
        "scores = [score_nb,score_knn,score_dt]\r\n",
        "algorithms = [\"Naive Bayes\",\"K-Nearest Neighbors\",\"Decision Tree\"]    \r\n",
        "\r\n",
        "for i in range(len(algorithms)):\r\n",
        "    print(\"The accuracy score achieved using \"+algorithms[i]+\" is: \"+str(scores[i])+\" %\")"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 55.79 %\n",
            "The accuracy score achieved using K-Nearest Neighbors is: 55.04 %\n",
            "The accuracy score achieved using Decision Tree is: 59.16 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "oZzxRLQ5sYL9",
        "outputId": "c4e77e31-a6c9-4173-a181-8f9227057d6c"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(15,8)})\r\n",
        "plt.xlabel(\"Algorithms\")\r\n",
        "plt.ylabel(\"Accuracy score\")\r\n",
        "sns.barplot(algorithms,scores)"
      ],
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d1770e588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}