Code_K-NN.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PatientID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Subluxation_percent</th>\n",
       "      <th>Femoral_neck_angle</th>\n",
       "      <th>Lateral_center_edge_angle</th>\n",
       "      <th>extrusion_index</th>\n",
       "      <th>Femoral_anteversion</th>\n",
       "      <th>Acetabular_anteversion</th>\n",
       "      <th>Anterior_sector_angle</th>\n",
       "      <th>Posterior_sector_angle</th>\n",
       "      <th>Cluster</th>\n",
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       "      <td>0.439726</td>\n",
       "      <td>138.7</td>\n",
       "      <td>17.3</td>\n",
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       "      <td>48.50</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>19.20</td>\n",
       "      <td>15.9</td>\n",
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       "      <td>19.9</td>\n",
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       "      <td>136.6</td>\n",
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       "      <td>36.10</td>\n",
       "      <td>10.5</td>\n",
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       "      <td>-5.3</td>\n",
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       "      <td>29.30</td>\n",
       "      <td>11.8</td>\n",
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      "text/plain": [
       "   PatientID  Gender  Subluxation_percent  Femoral_neck_angle  \\\n",
       "0          1       0             0.439726               138.7   \n",
       "1          2       1             0.000000               136.2   \n",
       "2          3       1             0.000000               139.9   \n",
       "3          4       0             0.484536               136.6   \n",
       "4          5       0             0.225806               153.7   \n",
       "\n",
       "   Lateral_center_edge_angle  extrusion_index  Femoral_anteversion  \\\n",
       "0                       17.3         0.456075                48.50   \n",
       "1                       11.5         0.349057                19.20   \n",
       "2                       11.5         0.295238                33.95   \n",
       "3                      -18.1         0.773196                36.10   \n",
       "4                       -5.3         0.512799                29.30   \n",
       "\n",
       "   Acetabular_anteversion  Anterior_sector_angle  Posterior_sector_angle  \\\n",
       "0                    12.0                   36.0                    69.4   \n",
       "1                    15.9                   55.5                    90.0   \n",
       "2                    19.9                   51.8                    94.2   \n",
       "3                    10.5                   42.3                    74.1   \n",
       "4                    11.8                   53.7                    76.7   \n",
       "\n",
       "   Cluster  \n",
       "0        0  \n",
       "1        1  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Read the file\n",
    "file_path = 'C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/All/data_with_clusters.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Display the first few rows\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e151bd70-1309-4da3-a854-ab3085841457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model: 0.66\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Select features and target variable\n",
    "features = ['Gender', 'Subluxation_percent', 'Femoral_neck_angle', 'Lateral_center_edge_angle', 'extrusion_index']\n",
    "X = data[features]\n",
    "y = data['Cluster']\n",
    "\n",
    "# Convert gender to numerical values if it's categorical\n",
    "if X['Gender'].dtype == 'object':\n",
    "    X['Gender'] = X['Gender'].astype('category').cat.codes\n",
    "\n",
    "# Split the data into training and testing sets (80% training, 20% testing)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Create the K-NN classifier\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=5) # You can change the number of neighbors\n",
    "\n",
    "# Train the model\n",
    "knn_classifier.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = knn_classifier.predict(X_test_scaled)\n",
    "\n",
    "# Compute the accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy of the model: {accuracy:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2c5ad48f-c333-49b6-bb51-d042669ab60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.66\n",
      "Precision: 0.78\n",
      "Recall: 0.73\n",
      "F1 Score: 0.75\n",
      "Misclassification Rate: 0.34\n",
      "AUC of ROC: 0.64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\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",
    "# AUC of ROC (assuming binary classification with labels 0 and 1)\n",
    "y_prob = knn_classifier.predict_proba(X_test_scaled)[:, 1] # Probability estimates of the positive class\n",
    "auc_roc = roc_auc_score(y_test, y_prob)\n",
    "print(f'AUC of ROC: {auc_roc:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3ee2ce16-95a3-405b-b6ba-2c5bd0dfffdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted clusters have been saved to C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/Prediction K_nearest_neighbors/internal_predicted_clusters.csv\n"
     ]
    }
   ],
   "source": [
    "# Create a new DataFrame with the test data and the predicted clusters\n",
    "predicted_data = X_test.copy()\n",
    "predicted_data['predicted_cluster'] = y_pred\n",
    "\n",
    "# Adding the ground truth cluster labels\n",
    "predicted_data['ground_truth_cluster'] = data['Cluster']\n",
    "\n",
    "# Save the DataFrame to a new CSV file\n",
    "output_file_path = 'C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/Prediction K_nearest_neighbors/internal_predicted_clusters.csv'\n",
    "predicted_data.to_csv(output_file_path, index=False)\n",
    "\n",
    "print(f'Predicted clusters have been saved to {output_file_path}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "17c1e973-bdd7-4583-aff9-ee88887ba26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "External Validation Metrics:\n",
      "Accuracy: 0.68\n",
      "Precision: 0.78\n",
      "Recall: 0.76\n",
      "F1 Score: 0.77\n",
      "Misclassification Rate: 0.32\n",
      "AUC of ROC: 0.67\n",
      "Predicted clusters for the external data have been saved to C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/Prediction K_nearest_neighbors/external_predicted_clusters.csv\n"
     ]
    }
   ],
   "source": [
    "##External validation\n",
    "# Read the external validation data\n",
    "external_file_path = 'C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/External validation/External_validation_data_withgroundtruth_Cluster.csv'\n",
    "external_data = pd.read_csv(external_file_path)\n",
    "\n",
    "# Select the same features as before\n",
    "external_X = external_data[features]\n",
    "\n",
    "# Assuming the target variable (ground truth) is named 'cluster'\n",
    "external_y = external_data['Groundtruth_Cluster']\n",
    "\n",
    "# Convert gender to numerical values if it's categorical\n",
    "if external_X['Gender'].dtype == 'object':\n",
    "    external_X['Gender'] = external_X['Gender'].astype('category').cat.codes\n",
    "\n",
    "# Scale the features using the same scaler as before\n",
    "external_X_scaled = scaler.transform(external_X)\n",
    "\n",
    "# Make predictions using the trained K-NN model\n",
    "external_y_pred = knn_classifier.predict(external_X_scaled)\n",
    "\n",
    "# Calculate the metrics\n",
    "external_accuracy = accuracy_score(external_y, external_y_pred)\n",
    "external_precision = precision_score(external_y, external_y_pred)\n",
    "external_recall = recall_score(external_y, external_y_pred)\n",
    "external_f1 = f1_score(external_y, external_y_pred)\n",
    "external_misclassification_rate = 1 - external_accuracy\n",
    "external_y_prob = knn_classifier.predict_proba(external_X_scaled)[:, 1]\n",
    "external_auc_roc = roc_auc_score(external_y, external_y_prob)\n",
    "\n",
    "# Print the results\n",
    "print(f'External Validation Metrics:')\n",
    "print(f'Accuracy: {external_accuracy:.2f}')\n",
    "print(f'Precision: {external_precision:.2f}')\n",
    "print(f'Recall: {external_recall:.2f}')\n",
    "print(f'F1 Score: {external_f1:.2f}')\n",
    "print(f'Misclassification Rate: {external_misclassification_rate:.2f}')\n",
    "print(f'AUC of ROC: {external_auc_roc:.2f}')\n",
    "\n",
    "# Create a new DataFrame with the external data and the predicted clusters\n",
    "external_predicted_data = external_X.copy()\n",
    "external_predicted_data['predicted_cluster'] = external_y_pred\n",
    "\n",
    "# Adding the ground truth cluster labels (y_external) to the external predicted data DataFrame\n",
    "external_predicted_data['ground_truth_cluster'] = external_data['Groundtruth_Cluster']\n",
    "\n",
    "# Save the DataFrame to a new CSV file\n",
    "external_output_file_path = 'C:/Work/AI_Sports_Medicine/Hip/DDH/Cluster/Prediction K_nearest_neighbors/external_predicted_clusters.csv'\n",
    "external_predicted_data.to_csv(external_output_file_path, index=False)\n",
    "\n",
    "print(f'Predicted clusters for the external data have been saved to {external_output_file_path}')\n"
   ]
  }
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