Code_SVM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f5d393d-4582-4721-ac6d-d88186a2c126",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b0166b4-d1a7-45fe-bcad-d588e230b2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[ 6 15]\n",
      " [ 0 52]]\n",
      "\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.29      0.44        21\n",
      "           1       0.78      1.00      0.87        52\n",
      "\n",
      "    accuracy                           0.79        73\n",
      "   macro avg       0.89      0.64      0.66        73\n",
      "weighted avg       0.84      0.79      0.75        73\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "# Load the data\n",
    "data = pd.read_csv(' ')\n",
    "\n",
    "# Select required parameters and target variable\n",
    "X = data[['Gender', 'Subluxation_percent', 'Femoral_neck_angle', 'Lateral_center_edge_angle', 'extrusion_index']]\n",
    "y = data['Cluster']\n",
    "\n",
    "# Split the data into training and testing sets with a 80/20 split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Create the SVM model\n",
    "model = svm.SVC(probability=True)\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
    "\n",
    "# Print the classification report\n",
    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6804d111-2b7d-4ac2-b95f-c15769888b62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7945205479452054, 1.0, 0.8739495798319328)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, f1_score\n",
    "\n",
    "# Calculate Accuracy, Recall and F1 Score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "accuracy, recall, f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8fb47c5-5c1a-43b6-90e1-d9077a64fef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.79\n",
      "Precision: 0.78\n",
      "Recall: 1.00\n",
      "F1 Score: 0.87\n",
      "Misclassification Rate: 0.21\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print(f'Precision: {precision:.2f}')\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f'Recall: {recall:.2f}')\n",
    "\n",
    "# F1 Score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f'F1 Score: {f1:.2f}')\n",
    "\n",
    "# Misclassification Rate\n",
    "misclassification_rate = 1 - accuracy\n",
    "print(f'Misclassification Rate: {misclassification_rate:.2f}')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a71c7668-f1be-47a8-ac4c-186eb6b70029",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make new csv file contating ground truth and predicted cluster for visualiztion\n",
    "# Make predictions on the training set and testing set\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffd5fbb0-be07-4735-a17f-2a1820c7262d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the predictions to the training and testing sets\n",
    "X_train = X_train.copy()\n",
    "X_test = X_test.copy()\n",
    "X_train['Predicted_Cluster'] = y_train_pred\n",
    "X_test['Predicted_Cluster'] = y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19b4f3df-25d3-4973-9511-f742eeaee72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column to indicate whether each instance is in the training set or the testing set\n",
    "X_train['Set'] = 'Train'\n",
    "X_test['Set'] = 'Test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2139ee5-bc93-460f-872b-73a77b2346a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the original data with the combined data\n",
    "merged_data = pd.merge(data, pd.concat([X_train, X_test])[['Predicted_Cluster', 'Set']], left_index=True, right_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6b29ce5-fd92-42a6-b2ca-33a90641c0b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the merged data to a CSV file\n",
    "merged_data.to_csv('', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "404f658a-e14f-4366-8de0-92d9186d7276",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute probabilities\n",
    "y_test_prob = model.predict_proba(X_test.drop(columns=['Predicted_Cluster', 'Set']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0272f3c1-46e9-4c7c-b835-c0c21ae953b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, RocCurveDisplay, precision_recall_curve, PrecisionRecallDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_test_prob[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Calculate Precision-Recall curve\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_test_prob[:, 1])\n",
    "\n",
    "# Plot ROC curve\n",
    "roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc).plot()\n",
    "\n",
    "# Plot Precision-Recall curve\n",
    "pr_display = PrecisionRecallDisplay(precision=precision, recall=recall).plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "14e0020b-eff8-43ac-b71f-6725b949ce42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "New Data Prediction: [1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\36819\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Define new data\n",
    "new_data = np.array([[1, 0.3, 135, 15, 0.4]])\n",
    "\n",
    "# Predict the cluster for the new data\n",
    "new_pred = model.predict(new_data)\n",
    "print(\"\\nNew Data Prediction:\", new_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ddf2ed0-bcfb-469b-a8fd-636672f823d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load external data\n",
    "new_data = pd.read_csv('C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/External validation/External_validation_data_withgroundtruth_Cluster.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "974e985c-6431-4a88-8f27-fc1cc9ba4cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Select features used for prediction\n",
    "new_X = new_data[['Gender', 'Subluxation_percent', 'Femoral_neck_angle', 'Lateral_center_edge_angle', 'extrusion_index']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a131b3c8-0945-4c9f-88fd-6d2a1a9e5414",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pred = model.predict(new_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "61d31392-07fc-4d99-8ca2-e5b5ba782c1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the prediction results to the DataFrame\n",
    "new_data['Predicted_Cluster'] = new_pred\n",
    "\n",
    "# Export the DataFrame to a new CSV file\n",
    "new_data.to_csv('C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/Prediction_SVM_Xrayparameters/external_validation_predictions.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "469803dc-dcbb-44e2-9a4b-7ff7b0265783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.79\n",
      "Precision: 0.78\n",
      "Recall: 1.00\n",
      "F1 Score: 0.87\n",
      "Misclassification Rate: 0.21\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.2f}')\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test, y_pred)\n",
    "print(f'Precision: {precision:.2f}')\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test, y_pred)\n",
    "print(f'Recall: {recall:.2f}')\n",
    "\n",
    "# F1 Score\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f'F1 Score: {f1:.2f}')\n",
    "\n",
    "# Misclassification Rate\n",
    "misclassification_rate = 1 - accuracy\n",
    "print(f'Misclassification Rate: {misclassification_rate:.2f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}