Predicting Cardiovascular Disease Effectively Using Various Parameters

Abstract

Cardiovascular diseases (CVDs) remain the world’s leading cause of mortality. Robust prediction models can identify high risk individuals early and inform preventive action. This paper reviews the evolution of CVD risk modelling, describes a comprehensive, multi parameter machine learning (ML) framework, and synthesises recent evidence on its performance. By integrating demographic, clinical, biochemical, behavioural, psychosocial, environmental, imaging and genomic factors, contemporary ML ensembles and large language model (LLM)–based systems achieve state of the art discrimination and calibration across diverse populations. Remaining gaps include explainability, data set bias, ethics and real world implementation.