Stroke Classification Comparison with KNN through Standardization and Normalization Techniques

Abstract

This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios. Demonstrating the importance of data scaling, both z-score and min-max achieve 95.07% accuracy. Notably, normalization averages a higher accuracy (94.25%) than standardization (94.21%), highlighting the critical role of data scaling for robust machine learning performance and informed health decisions.