An evolutionary machine learning algorithm for cardiovascular disease risk prediction.

ABSTRACT:

Introduction

This study developed a novel risk assessment model to predict the occurrence of cardiovascular disease (CVD) events. It uses a Genetic Algorithm (GA) to develop an easy-to-use model with high accuracy, calibrated based on the Isfahan Cohort Study (ICS) database.

Methods

The ICS was a population-based prospective cohort study of 6,504 healthy Iranian adults aged ≥ 35 years followed for incident CVD over ten years, from 2001 to 2010. To develop a risk score, the problem of predicting CVD was solved using a well-designed GA, and finally, the results were compared with classic machine learning (ML) and statistical methods.

Results

A number of risk scores such as the WHO, and PARS models were utilized as the baseline for comparison due to their similar chart-b

SUBMITTER: Ordikhani M 

PROVIDER: S-EPMC9333440 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature