Machine Learning Stroke Prediction in Smart Healthcare: Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

Abstract

This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision and recall of 96.3%, and a Receiver Operating Characteristics (ROC) area of 96.2%, surpassing the performance of the Chi-Square F-KNN model, which showed an accuracy of 95%. These results highlight that BFS improves the ability to select the most relevant features, contributing to more reliable and accurate stroke predictions. The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications, leading to better stroke risk management and improved patient outcomes.