Detecting Parkinson’s Disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58263ba4",
   "metadata": {},
   "source": [
    "# O que é a Doença de Parkinson?\n",
    "\n",
    "A doença de Parkinson é um distúrbio progressivo do sistema nervoso central que afeta o movimento e induz tremores e rigidez. Tem 5 estágios e afeta mais de 1 milhão de pessoas todos os anos na Índia. Isso é crônico e ainda não tem cura. É uma doença neurodegenerativa que afeta os neurônios produtores de dopamina no cérebro."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9978683f",
   "metadata": {},
   "source": [
    "# O que é XGBoost?\n",
    "\n",
    "Bom, XGBoost é um novo algoritmo de aprendizado de máquina projetado com velocidade e desempenho em mente. XGBoost significa eXtreme Gradient Boosting e é baseado em árvores de decisão. Neste projeto, importaremos o XGBClassifier da biblioteca xgboost."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15d55c4",
   "metadata": {},
   "source": [
    "# Objetivo\n",
    "\n",
    "Construir um modelo para detectar com precisão a presença da doença de Parkinson em um indivíduo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f076c1",
   "metadata": {},
   "source": [
    "# Importações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1207ae4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
   "execution_count": 2,
   "id": "4166bb5a",
   "metadata": {},
   "outputs": [
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       "    <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>...</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",
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       "      <td>119.992</td>\n",
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       "      <td>74.997</td>\n",
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       "      <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>...</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",
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       "    <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>...</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>...</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>...</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>...</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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       "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
       "0           0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
       "1           0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
       "2           0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
       "3           0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
       "4           0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
       "\n",
       "   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
       "0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
       "1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
       "2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
       "3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
       "4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
       "\n",
       "    spread2        D2       PPE  \n",
       "0  0.266482  2.301442  0.284654  \n",
       "1  0.335590  2.486855  0.368674  \n",
       "2  0.311173  2.342259  0.332634  \n",
       "3  0.334147  2.405554  0.368975  \n",
       "4  0.234513  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 24 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/parkinsons.data\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b29d918",
   "metadata": {},
   "source": [
    "## Pegando do DataFrame o que vão ser os nossos labels e as nossas features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4b21485",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features:  [[119.992 157.302 74.997 ... 0.266482 2.301442 0.284654]\n",
      " [122.4 148.65 113.819 ... 0.33559 2.486855 0.368674]\n",
      " [116.682 131.111 111.555 ... 0.311173 2.342259 0.332634]\n",
      " ...\n",
      " [174.688 240.005 74.287 ... 0.158453 2.679772 0.131728]\n",
      " [198.764 396.961 74.904 ... 0.207454 2.138608 0.123306]\n",
      " [214.289 260.277 77.973 ... 0.190667 2.555477 0.148569]]\n",
      "labels:  [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1\n",
      " 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "features = df.loc[:, df.columns != 'status'].values[:, 1:]\n",
    "labels = df.loc[:, 'status'].values\n",
    "print(\"features: \", features)\n",
    "print(\"labels: \", labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c20daa9",
   "metadata": {},
   "source": [
    "## Contagem dos dados na coluna 'status' nos temos 0 e 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9b16fe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valores 0:  48\n",
      "Valores 1:  147\n"
     ]
    }
   ],
   "source": [
    "print(\"valores 0: \", labels[labels == 0].shape[0])\n",
    "print(\"Valores 1: \", labels[labels == 1].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9fd432",
   "metadata": {},
   "source": [
    "## Normalizar as 'features' \n",
    "Utilizando o MinMaxScaler nos conseguimos normalizar os dados em uma range de (-1,1) e depois justar os dados e transformalos usando o metodo fit_transform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "192cd906",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler((-1,1))\n",
    "x = scaler.fit_transform(features)\n",
    "y = labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ca89d9",
   "metadata": {},
   "source": [
    "## Agora, nos vamos dividir os dados do dataset entre dados de teste e dados de treino. Deixando 20% dos dados para treino."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dcfe753",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2009129f",
   "metadata": {},
   "source": [
    "## Agora é so inicializar o XGBClassifier e treinar o modelo. \n",
    "\n",
    "Esse classificador usa eXtreme Gradient Boosting. Ele se enquadra na categoria de Ensemble Learning em ML, onde treinamos e prevemos o uso de muitos modelos para produzir um resultado superior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cc81fbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:33:28] WARNING: ../src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raphael720/anaconda3/lib/python3.9/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelo = XGBClassifier()\n",
    "modelo.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c11fc5",
   "metadata": {},
   "source": [
    "## Por fim, nossa previsão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2629e0af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuracia do nosso modelo:  94.87179487179486\n"
     ]
    }
   ],
   "source": [
    "y_pred = modelo.predict(x_test)\n",
    "print(\"Acuracia do nosso modelo: \", accuracy_score(y_test, y_pred) * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf676d0",
   "metadata": {},
   "source": [
    "# Referências do projeto\n",
    "\n",
    "- https://scikit-learn.org/stable/\n",
    "- https://xgboost.readthedocs.io/en/stable/python/python_intro.html\n",
    "- https://data-flair.training/blogs/gradient-boosting-algorithm/\n",
    "- https://data-flair.training/blogs/python-machine-learning-project-detecting-parkinson-disease/\n",
    "- https://drauziovarella.uol.com.br/doencas-e-sintomas/doenca-de-parkinson/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfc71b98",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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