Comparative Study of Logistic Regression and K-Nearest Neighbor for Heart Disease Prediction

This study aims to analyze the performance comparison between two classification algorithms, Logistic Regression and K-Nearest Neighbors (KNN), in predicting heart disease using the available dataset. Heart disease is one of the leading causes of death worldwide, making it essential to develop an effective prediction system. Logistic Regression and KNN were selected due to their proven effectiveness in previous studies. The research process began with data preprocessing, followed by data splitting using K-Fold Cross Validation, and evaluation using accuracy, precision, recall, and f1-score metrics. The results showed that Logistic Regression outperformed KNN, achieving an accuracy of 89%, compared to KNN’s accuracy of 80%. Logistic Regression also demonstrated higher consistency in capturing patterns within the dataset. Therefore, Logistic Regression is considered more effective in predicting heart disease compared to KNN in this study.