Model_latest.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GpY-ArzB1rH-"
   },
   "source": [
    "# **Disease Detection using Symptoms and Treatment recommendation**\n",
    "\n",
    "This notebook contains all the models applied on the scraped dataset and contains the accuracy comparison plot.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lEsjGGNblruf"
   },
   "outputs": [],
   "source": [
    "# all necessary imports\n",
    "import warnings\n",
    "from decimal import Decimal\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t1sbUx8C22zG"
   },
   "source": [
    "**Disease Symptom dataset** was created in a separate python program.\n",
    "\n",
    "**Dataset scrapping** was done using **NHP website** and **wikipedia data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xTIhMk1Ilz7O"
   },
   "outputs": [],
   "source": [
    "# Load Dataset scraped from NHP (https://www.nhp.gov.in/disease-a-z) & Wikipedia\n",
    "# Scrapping and creation of dataset csv is done in a separate program\n",
    "df_comb = pd.read_csv(\"Dataset/dis_sym_dataset_comb.csv\")\n",
    "\n",
    "# creation of features and label for training the models\n",
    "X = df_comb.iloc[:, 1:]\n",
    "Y = df_comb.iloc[:, 0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "   abdominal cramp  abdominal distention  abnormal behavior  \\\n",
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  label_dis\n",
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   "source": [
    "Y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wB98cbQc3kX1"
   },
   "source": [
    "*Train Test Split*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "43edxNr-l37s"
   },
   "outputs": [],
   "source": [
    "# splitting data for training the classifiers and testing\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NFWxSoKRl6iU"
   },
   "outputs": [],
   "source": [
    "# lists used for accuracy plots\n",
    "accuracy_list = []\n",
    "cross_accuracy_list = []\n",
    "model_list = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "50u3FJaH3r0k"
   },
   "source": [
    "# **Multinomial Naive Bayes Classifier**\n",
    "\n",
    "MNB works better on discrete features and the multinomial distribution usually requires integer counts. However, it also works with fractional counts such tf-idf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "OUrShCRcl8Gs",
    "outputId": "fa8d8306-89bf-4c5d-e23f-58a60d0bf647"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (MNB) : 84.73%\n",
      "Cross Validation Accuracy (MNB): 84.50%\n"
     ]
    }
   ],
   "source": [
    "# Multinomial NB Classifier\n",
    "mnb = MultinomialNB()\n",
    "mnb = mnb.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "mnb_pred = mnb.predict(x_test)\n",
    "# calculation of accuracy score based on predictions performed\n",
    "# converting to Decimal as rounding with float is inaccurate\n",
    "acc_mnb = round(Decimal(accuracy_score(y_test, mnb_pred) * 100), 2)\n",
    "accuracy_list.append(acc_mnb)\n",
    "model_list.append(\"MNB\")\n",
    "print(f\"Accuracy (MNB) : {acc_mnb}%\")\n",
    "\n",
    "# Cross Validation Accuracy MNB\n",
    "# performing cross validation with 5 different splits\n",
    "scores_mnb = cross_val_score(mnb, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_mnb.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (MNB): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gEJWlm2u3yRc"
   },
   "source": [
    "# **Random Forest Classifier**\n",
    "\n",
    "RF classifier is an estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and take the average to improve predicting accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "owiCcnlvmAP9",
    "outputId": "0e011b23-5965-4fa7-ee6d-416ebc2070ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (RF) : 91.29%\n",
      "Cross Validation Accuracy (RF): 86.84%\n"
     ]
    }
   ],
   "source": [
    "# RF Classifier\n",
    "rf = RandomForestClassifier(n_estimators=10, criterion='entropy')\n",
    "rf = rf.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "rf_pred = rf.predict(x_test)\n",
    "acc_rf = round(Decimal(accuracy_score(y_test, rf_pred) * 100), 2)\n",
    "accuracy_list.append(acc_rf)\n",
    "model_list.append(\"RF\")\n",
    "print(f\"Accuracy (RF) : {acc_rf}%\")\n",
    "\n",
    "# Cross Validation Accuracy RF\n",
    "# performing cross validation with 5 different splits\n",
    "scores_rf = cross_val_score(rf, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_rf.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (RF): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xxdvB5Bz9yEx"
   },
   "source": [
    "# **K-Nearest Neighbor Classifier**\n",
    "\n",
    "The principle behind nearest neighbor method is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. No. of samples used here is 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "OfIMLCfPmCqr",
    "outputId": "2040dc76-c519-433e-e558-504b38f9d227"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (KNN) : 91.06%\n",
      "Cross Validation Accuracy (KNN): 85.25%\n"
     ]
    }
   ],
   "source": [
    "# KNN Classifier\n",
    "knn = KNeighborsClassifier(n_neighbors=7, weights='distance', n_jobs=4)\n",
    "knn = knn.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "knn_pred = knn.predict(x_test)\n",
    "acc_knn = round(Decimal(accuracy_score(y_test, knn_pred) * 100), 2)\n",
    "accuracy_list.append(acc_knn)\n",
    "model_list.append(\"KNN\")\n",
    "print(f\"Accuracy (KNN) : {acc_knn}%\")\n",
    "\n",
    "# Cross Validation Accuracy KNN\n",
    "# performing cross validation with 5 different splits\n",
    "scores_knn = cross_val_score(knn, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_knn.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (KNN): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x4j2f2dH94dU"
   },
   "source": [
    "# **Logistic Regression Classifier**\n",
    "\n",
    "Logistic Regression uses OvR (One Vs Rest) approach when classification is multi class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "-ZBVt3BlmE7c",
    "outputId": "173b9591-29c3-4ad4-9139-1bcf2102b9e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (LR) : 90.61%\n",
      "Cross Validation Accuracy (LR): 89.19%\n"
     ]
    }
   ],
   "source": [
    "# LR Classifier\n",
    "lr = LogisticRegression()\n",
    "lr = lr.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "lr_pred = lr.predict(x_test)\n",
    "acc_lr = round(Decimal(accuracy_score(y_test, lr_pred) * 100), 2)\n",
    "accuracy_list.append(acc_lr)\n",
    "model_list.append(\"LR\")\n",
    "print(f\"Accuracy (LR) : {acc_lr}%\")\n",
    "\n",
    "# Cross Validation Accuracy LR\n",
    "# performing cross validation with 5 different splits\n",
    "scores_lr = cross_val_score(lr, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_lr.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (LR): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zbz-aL_Q9_U0"
   },
   "source": [
    "# **Support Vector Machine Classifier**\n",
    "\n",
    "SVM follows OvO (One Vs One) scheme when dealing with multi class classification. SVM classifier works by drawing a straight line between two classes. All the data points that fall on one side of the line will be labeled as one class and all the points that fall on the other side will be labeled as the second. This seems simple for binary class but the complexity is increased when we have multiple classes. In our classification problem we have 261 classes (equal to diseases)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "JDSJc0RlmG-0",
    "outputId": "ee7dc68c-51a3-4630-ed30-9a421fb43ba4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (SVM) : 89.71%\n",
      "Cross Validation Accuracy (SVM): 88.62%\n"
     ]
    }
   ],
   "source": [
    "# SVM Classifier\n",
    "svm = SVC()\n",
    "svm = svm.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "svm_pred = svm.predict(x_test)\n",
    "acc_svm = round(Decimal(accuracy_score(y_test, svm_pred) * 100), 2)\n",
    "accuracy_list.append(acc_svm)\n",
    "model_list.append(\"SVM\")\n",
    "print(f\"Accuracy (SVM) : {acc_svm}%\")\n",
    "\n",
    "# Cross Validation Accuracy SVM\n",
    "# performing cross validation with 5 different splits\n",
    "scores_svm = cross_val_score(svm, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_svm.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (SVM): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uUYxizhO-FtC"
   },
   "source": [
    "# **Decision Tree Classifier**\n",
    "\n",
    "DT is one of the easiest and popular classification algorithms to understand and interpret. It can be utilized for both classification and regression. DT follows a tree like structure to reach to a decision whether the given sample under consideration belong to which class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "CwFDYYBQmKAc",
    "outputId": "fca7c327-b54d-4ffb-9eb8-4711372773cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (DT) : 91.29%\n",
      "Cross Validation Accuracy (DT): 83.59%\n"
     ]
    }
   ],
   "source": [
    "# DT Classifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt = dt.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "dt_pred = dt.predict(x_test)\n",
    "acc_dt = round(Decimal(accuracy_score(y_test, dt_pred) * 100), 2)\n",
    "accuracy_list.append(acc_dt)\n",
    "model_list.append(\"DT\")\n",
    "print(f\"Accuracy (DT) : {acc_dt}%\")\n",
    "\n",
    "# Cross Validation Accuracy DT\n",
    "# performing cross validation with 5 different splits\n",
    "scores_dt = cross_val_score(dt, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_dt.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (DT): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2zI71-uw-JMq"
   },
   "source": [
    "# **Multilayer Perceptron Classifier**\n",
    "\n",
    "MLP is a feed forward ANN (Artificial Neural Network) which is extensively used for classification purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "nwVwjevImMQU",
    "outputId": "18989cdc-3840-47c7-92dd-f5775446042a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy (MLP) : 90.95%\n",
      "Cross Validation Accuracy (MLP): 87.12%\n"
     ]
    }
   ],
   "source": [
    "# MLP Classifier\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(32, 32, 32), activation='relu', solver='adam', max_iter=50)\n",
    "mlp = mlp.fit(X, Y)\n",
    "# prediction of labels for the test data\n",
    "mlp_pred = mlp.predict(x_test)\n",
    "acc_mlp = round(Decimal(accuracy_score(y_test, mlp_pred) * 100), 2)\n",
    "accuracy_list.append(acc_mlp)\n",
    "model_list.append(\"MLP\")\n",
    "print(f\"Accuracy (MLP) : {acc_mlp}%\")\n",
    "\n",
    "# Cross Validation Accuracy MLP\n",
    "# performing cross validation with 5 different splits\n",
    "scores_mlp = cross_val_score(mlp, X, Y, cv=5)\n",
    "# mean of cross val score (accuracy)\n",
    "score = round(Decimal(scores_mlp.mean() * 100), 2)\n",
    "cross_accuracy_list.append(score)\n",
    "print(f\"Cross Validation Accuracy (MLP): {score}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x0deyBf7-QSD"
   },
   "source": [
    "# **Accuracy Comparison Plot for all the classifiers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 647
    },
    "colab_type": "code",
    "id": "c_4WiPo4mR2M",
    "outputId": "e233f609-b457-47cc-cdaa-9905da4a9355"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparison plot for all classifiers with their accuracy\n",
    "plt.style.use('seaborn-poster')\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot()\n",
    "plt.title(\"Model Vs Accuracy\")\n",
    "plt.xlabel('Classifier')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.bar(model_list, accuracy_list, color='green')\n",
    "for i, j in enumerate(accuracy_list):\n",
    "    ax.text(float(i)-0.15, float(j)+0.7, str(j), color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ja6vRv2M-l_s"
   },
   "source": [
    "# **Cross Validation Accuracy Comparison Plot for all the classifiers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 647
    },
    "colab_type": "code",
    "id": "8WmaR3XBzT0e",
    "outputId": "77a1a225-6af3-4088-8414-c29ae6a621da"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparison plot for all classifiers with their accuracy\n",
    "plt.style.use('seaborn-poster')\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot()\n",
    "plt.title(\"Model Vs Cross Validation Accuracy\")\n",
    "plt.xlabel('Classifier')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.bar(model_list, cross_accuracy_list, color='yellow')\n",
    "for i, j in enumerate(cross_accuracy_list):\n",
    "    ax.text(float(i)-0.15, float(j)+0.7, str(j), color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Model_latest.ipynb",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}