Enhancing Support Vector Machine Performance for Heart Attack Prediction using RobustScaler-Based Outlier Handling

Abstract

Cardiovascular disease remains the leading cause of death worldwide, with most cases attributed to heart attacks and strokes. Early detection is crucial, yet conventional diagnostic methods are often constrained by time, cost, and uneven distribution of clinical expertise. Consequently, machine learning-based approaches offer a promising alternative for efficiently supporting heart attack prediction. This study employs the Support Vector Machine (SVM) algorithm, focusing on enhancing its performance through RobustScaler as a preprocessing technique to address outliers common in medical datasets. The objective of this study is to evaluate the impact of RobustScaler on SVM performance in heart attack classification. The model was developed using a dataset of 303 patient records, consisting of eight numerical features and one binary target label. Experiments were conducted under two preprocessing scenarios: without scaling (baseline) and with RobustScaler. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that applying RobustScaler significantly improves model performance, with accuracy increasing from 64.77% to 85.23%, representing a 20.46% improvement, and ROC-AUC rising from 73.65% to 93.36%, indicating a 26.78% increase in discriminatory ability. Additionally, recall for the negative class improved dramatically from 26.47% to 99.02%, reflecting better sensitivity in identifying non-heart attack cases. These findings demonstrate that proper preprocessing, particularly using RobustScaler, plays a vital role in optimizing SVM performance, especially when handling clinical data with extreme values