Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data

TL;DR

This study develops and compares multiple machine learning models using UK Biobank data to improve early detection of cardiovascular diseases, considering diverse demographic, socioeconomic, and imaging data.

Contribution

It introduces a comprehensive comparison of nine explainable machine learning models for CVD detection using primary healthcare data from a large UK cohort.

Findings

RF and NN achieved the highest accuracy and AUC scores.

Explainable models provided interpretable insights into CVD risk factors.

Models demonstrated robust performance across diverse subgroups.

Abstract

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves.