parkinson_disease_detection_using_XGBoost.ipynb

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    "colab": {
      "name": "parkinson disease detection using XGBoost.ipynb",
      "provenance": []
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      "source": [
        "#importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os, sys\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('parkinsons.data')\n",
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Ty9l-TtTDKB4",
        "outputId": "b07fa01b-a22a-4182-b593-b90f58f46f84"
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      "execution_count": 18,
      "outputs": [
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <th>Shimmer:APQ3</th>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
              "      <th>RPDE</th>\n",
              "      <th>DFA</th>\n",
              "      <th>spread1</th>\n",
              "      <th>spread2</th>\n",
              "      <th>D2</th>\n",
              "      <th>PPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>phon_R01_S01_1</td>\n",
              "      <td>119.992</td>\n",
              "      <td>157.302</td>\n",
              "      <td>74.997</td>\n",
              "      <td>0.00784</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.00554</td>\n",
              "      <td>0.01109</td>\n",
              "      <td>0.04374</td>\n",
              "      <td>0.426</td>\n",
              "      <td>0.02182</td>\n",
              "      <td>0.03130</td>\n",
              "      <td>0.02971</td>\n",
              "      <td>0.06545</td>\n",
              "      <td>0.02211</td>\n",
              "      <td>21.033</td>\n",
              "      <td>1</td>\n",
              "      <td>0.414783</td>\n",
              "      <td>0.815285</td>\n",
              "      <td>-4.813031</td>\n",
              "      <td>0.266482</td>\n",
              "      <td>2.301442</td>\n",
              "      <td>0.284654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>phon_R01_S01_2</td>\n",
              "      <td>122.400</td>\n",
              "      <td>148.650</td>\n",
              "      <td>113.819</td>\n",
              "      <td>0.00968</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00696</td>\n",
              "      <td>0.01394</td>\n",
              "      <td>0.06134</td>\n",
              "      <td>0.626</td>\n",
              "      <td>0.03134</td>\n",
              "      <td>0.04518</td>\n",
              "      <td>0.04368</td>\n",
              "      <td>0.09403</td>\n",
              "      <td>0.01929</td>\n",
              "      <td>19.085</td>\n",
              "      <td>1</td>\n",
              "      <td>0.458359</td>\n",
              "      <td>0.819521</td>\n",
              "      <td>-4.075192</td>\n",
              "      <td>0.335590</td>\n",
              "      <td>2.486855</td>\n",
              "      <td>0.368674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>phon_R01_S01_3</td>\n",
              "      <td>116.682</td>\n",
              "      <td>131.111</td>\n",
              "      <td>111.555</td>\n",
              "      <td>0.01050</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.00781</td>\n",
              "      <td>0.01633</td>\n",
              "      <td>0.05233</td>\n",
              "      <td>0.482</td>\n",
              "      <td>0.02757</td>\n",
              "      <td>0.03858</td>\n",
              "      <td>0.03590</td>\n",
              "      <td>0.08270</td>\n",
              "      <td>0.01309</td>\n",
              "      <td>20.651</td>\n",
              "      <td>1</td>\n",
              "      <td>0.429895</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-4.443179</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>2.342259</td>\n",
              "      <td>0.332634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>phon_R01_S01_4</td>\n",
              "      <td>116.676</td>\n",
              "      <td>137.871</td>\n",
              "      <td>111.366</td>\n",
              "      <td>0.00997</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00502</td>\n",
              "      <td>0.00698</td>\n",
              "      <td>0.01505</td>\n",
              "      <td>0.05492</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.02924</td>\n",
              "      <td>0.04005</td>\n",
              "      <td>0.03772</td>\n",
              "      <td>0.08771</td>\n",
              "      <td>0.01353</td>\n",
              "      <td>20.644</td>\n",
              "      <td>1</td>\n",
              "      <td>0.434969</td>\n",
              "      <td>0.819235</td>\n",
              "      <td>-4.117501</td>\n",
              "      <td>0.334147</td>\n",
              "      <td>2.405554</td>\n",
              "      <td>0.368975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>phon_R01_S01_5</td>\n",
              "      <td>116.014</td>\n",
              "      <td>141.781</td>\n",
              "      <td>110.655</td>\n",
              "      <td>0.01284</td>\n",
              "      <td>0.00011</td>\n",
              "      <td>0.00655</td>\n",
              "      <td>0.00908</td>\n",
              "      <td>0.01966</td>\n",
              "      <td>0.06425</td>\n",
              "      <td>0.584</td>\n",
              "      <td>0.03490</td>\n",
              "      <td>0.04825</td>\n",
              "      <td>0.04465</td>\n",
              "      <td>0.10470</td>\n",
              "      <td>0.01767</td>\n",
              "      <td>19.649</td>\n",
              "      <td>1</td>\n",
              "      <td>0.417356</td>\n",
              "      <td>0.823484</td>\n",
              "      <td>-3.747787</td>\n",
              "      <td>0.234513</td>\n",
              "      <td>2.332180</td>\n",
              "      <td>0.410335</td>\n",
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            "text/plain": [
              "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  ...   spread2        D2       PPE\n",
              "0  phon_R01_S01_1      119.992       157.302  ...  0.266482  2.301442  0.284654\n",
              "1  phon_R01_S01_2      122.400       148.650  ...  0.335590  2.486855  0.368674\n",
              "2  phon_R01_S01_3      116.682       131.111  ...  0.311173  2.342259  0.332634\n",
              "3  phon_R01_S01_4      116.676       137.871  ...  0.334147  2.405554  0.368975\n",
              "4  phon_R01_S01_5      116.014       141.781  ...  0.234513  2.332180  0.410335\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
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    {
      "cell_type": "code",
      "source": [
        "features=df.loc[:,df.columns!='status'].values[:,1:]\n",
        "\n",
        "labels=df.loc[:,'status'].values\n",
        "\n",
        "print(labels[labels==1].shape[0], labels[labels==0].shape[0])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_96I2-LDtaw",
        "outputId": "08584d49-e2b9-4ab7-93a4-21615de4d2e3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "147 48\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler=MinMaxScaler((-1,1))\n",
        "\n",
        "x=scaler.fit_transform(features)\n",
        "\n",
        "y=labels\n",
        "\n",
        "x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.2, random_state=7)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "l0WSzueLdhYH"
      },
      "execution_count": 20,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "model=XGBClassifier(eval_metric='mlogloss')\n",
        "\n",
        "model.fit(x_train,y_train)\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y5joWhRFd3zI",
        "outputId": "86fa7402-ecb6-4d23-864f-cb7620df8c7c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(eval_metric='mlogloss')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=model.predict(x_test)\n",
        "\n",
        "print(accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6_B-Wwqeql4",
        "outputId": "df4c6344-a5b6-4d63-b67d-2f9ba45be40b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "94.87179487179486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "pd.DataFrame(\n",
        "\n",
        "    confusion_matrix(y_test, y_pred),\n",
        "\n",
        "    columns=['Predicted Healthy', 'Predicted Parkinsons'],\n",
        "\n",
        "    index=['True Healthy', 'True Parkinsons']\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "33CszrIMfJ1T",
        "outputId": "a860649d-401b-433f-829a-790c0403d5d9"
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      "execution_count": 23,
      "outputs": [
        {
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              "      <th>Predicted Healthy</th>\n",
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              "      <th>True Healthy</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
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              "      <th>True Parkinsons</th>\n",
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              "      <td>32</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-77ded7b2-95ef-464e-82f0-4445f93869cf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-77ded7b2-95ef-464e-82f0-4445f93869cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 Predicted Healthy  Predicted Parkinsons\n",
              "True Healthy                     5                     2\n",
              "True Parkinsons                  0                    32"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}