Causes and remedies for persistent predictive limitations in heart disease prediction

Determine the primary causes of persistent predictive limitations in machine-learning-based heart disease prediction and develop solutions that substantially lower error rates and increase prediction accuracy.

Background

The paper compares ten machine-learning classifiers, including support vector machines and Simple CART, on UCI and Kaggle heart disease datasets using accuracy, precision, recall, F-measure, mean absolute error, and relative absolute error. Although the experiments identify strong-performing classifiers for each dataset, the conclusion acknowledges that the broader objective of substantially reducing prediction errors and improving accuracy has not been achieved definitively.

The unresolved issue concerns both explanation and remediation: the primary causes of persistent predictive limitations in heart disease prediction remain undetermined, and effective solutions for overcoming those limitations have not yet been realized. Addressing it would support more reliable and clinically useful machine-learning prediction systems.