Data-Driven Stroke Prediction: Leveraging KNN for Early Detection in Clinical Datasets

Abstract

This study explores the application of the K-Nearest Neighbors (KNN) algorithm in predicting stroke risk using a healthcare dataset from Kaggle containing 5,110 patient records with demographic and clinical attributes. Preprocessing steps included categorical encoding, normalization, and feature engineering, followed by model training using a pipeline that incorporated SMOTE to address class imbalance. Hyperparameter tuning via GridSearchCV improved model performance. The final KNN model achieved an accuracy of 84%, with a recall of 0.64 and precision of 0.18 for the stroke class, indicating moderate sensitivity but significant false positive rates typical of imbalanced medical datasets. Evaluation metrics included the confusion matrix, ROC curve (AUC = 0.82), and precision-recall curve (AP = 0.19). While the model demonstrates reasonable discriminative ability, the low precision suggests limited utility as a standalone diagnostic tool. These findings contribute to the growing body of research on machine learning for clinical decision support, emphasizing the need for balanced performance metrics in imbalanced datasets and highlighting KNN's potential as a preliminary screening instrument when combined with clinical judgment.