Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis

TL;DR

This paper presents an explainable machine learning framework that combines classification and regression techniques to improve cardiovascular disease diagnosis and risk prediction, utilizing data augmentation and explainability methods.

Contribution

It introduces a unified approach integrating classification and regression models with data balancing and explainability for cardiovascular diagnosis and prognosis.

Findings

Random Forest achieved 97.2% accuracy in classification.

Linear regression attained R2 of 0.992 in risk prediction.

SMOTE effectively balanced the dataset, enhancing model performance.

Abstract

Heart disease continues to pose a critical worldwide health issue, more specifically in areas with insufficient access to healthcare infrastructure and diagnostic systems. Conventional diagnostic approaches often fall short in accurately detecting and managing heart disease risks, resulting in unfavorable outcomes. Machine learning presents a powerful means to boost the precision and reliability of cardiovascular disease prognosis and diagnosis. In this research, we introduced a unified approach incorporating classification techniques for detecting heart disease and regression techniques for forecasting associated risks. The analysis utilized the dataset, named Heart Disease, containing 1,035 instances. To mitigate the problem of data disproportion, the SMOTE was implemented, producing 100,000 additional synthetic samples. Evaluation metrics such as F1-score, recall, precision, accuracy, MAE, RMSE, MSE, and R2 were adopted to evaluate the performance of the models. Among the classification algorithms, Random Forest delivered the most notable results, attaining an accuracy of 0.972 on actual data and 0.976 on artificially generated data. For prediction modeling, for both synthetic and real samples, linear regression produced the best R2 values of 0.992 and 0.984, respectively, along with the least amount of measurement errors. Furthermore, Explainable AI methods were utilized to improve the comprehensibility of the model outcomes. This paper emphasizes the transformative capabilities of machine learning for diagnosing cardiovascular disease and estimating risk levels, thereby supporting timely interventions and enhancing clinical settings.