ADVANCING PREDICTIVE MODELING OF HEART DISEASE THROUGH DATA ANALYSIS AND FEATURE ENGINEERING
Abstract
Cardiovascular disease continues to be a leading cause of worldwide morbidity and mortality and is an ongoing global health issue. The importance of early detection is key to survival, as an early diagnosis improves survival rates. Heart disease can be hard to diagnose because it involves various risk factors, which include age, sex, cholesterol, blood sugar level, and heart rate.
This study assesses the performance of cutting-edge machine learning and deep learning methods for early heart disease prediction. Using the CHD dataset, the research employs feature augmentation and selection techniques to improve predictive accuracy and simplify data of the models considered SVM with the Jellyfish Optimization Algorithm showed the best performance, within AUC of 98.47% and an accuracy rate of 94.48%.
Compared to the current methods, the suggested model demonstrates significant improvement. Precisely, it provides a 4.4% improvement in accuracy over the conventional methods and has an overall accuracy of 90% across larger test situations.
These results demonstrate the potential of high capacity of ML-based approaches in providing early diagnosis of heart disease and ultimately bettering patient outcomes.
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