Machine learning in coronary heart disease prediction: Structural equation modelling approach

Abstract

This research is an application of machine learning in medical sciences. The purpose of this research was to use machine learning through the simulated data to study the association of age, body mass index, cigarettes smoked per day, alcohol consumed per week, diastolic blood pressure, and systolic blood pressure on hypertension and coronary heart disease. The Structural Equation Modelling using Partial Least Square Method was used for the analysis of data. The results have revealed that except for age, body mass index and systolic blood pressure all the rest of the factors had a significant positive association with hypertension and coronary heart disease. The results can be of use for medical practitioners as well as researchers in machine learning, as it adds to the repository of earlier studies, which have attempted to seek relationships between these variables.

Keywords

• coronary heart disease
• hypertension
• data-driven models
• partial least square method
• predictive models
• health management