Cardiovascular Disease Long-Term Care Risk Prediction by Claims Data Analysis Using Machine Learning

Abstract

Heart complaint is a major global health concern, especially in prognosticating cardiovascular issues. Machine literacy (ML) and the Internet of effects (IoT) offer new ways to dissect healthcare data. still, current exploration lacks depth in using ML for heart complaint vaticination. To fill this gap, we propose a unique system that uses ML to identify crucial features for better heart complaint vaticination delicacy. Our model combines colorful features and bracket ways to achieve an delicacy of 88.7 in prognosticating heart complaint, with the cold-blooded arbitrary timber and direct model (HRFLM) proving particularly effective. This study advances heart complaint discovery by integrating ML and IoT technologies.