Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis

Abstract

Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments into the RF model's predictive capabilities. We recruited 606 patients from the Department of Nuclear Medicine at the University Hospital of Patras, Greece, from 16 February 2018 to 28 February 2022. Clinical data inputs included age, sex, comprehensive cardiovascular history (including prior myocardial infarction and revascularisation), CAD predisposing factors (such as hypertension, dyslipidemia, smoking, diabetes, and peripheral arteriopathy), baseline ECG abnormalities, and symptomatic descriptions ranging from asymptomatic states to angina-like symptoms and dyspnea on exertion. The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges' assessments) were comparable at 79% and 80.17%, respectively. However, the performance of the RF model notably declined to 73.76% when human clinical judgments were excluded from its training dataset. These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics.

Keywords: coronary artery disease; machine learning; probability calibration; random forest.

Figure 1

Research methodology.

Figure 2

Box plots of assigned probability categories. ML score refers to the RF model trained with the experts’ diagnosis as an additional feature.

Figure 3

Assigned CAD-probability categories per patient case and their true labels. Patient cases are sorted by label across the x-axis. (a) complete dataset (b) grey zone.

Figure 4

Probability scores of ML per patient case. Purple colour: CAD, yellow: Healthy. (a) when using the expert’s diagnosis as an additional input feature, (b) without using the expert’s diagnosis as an additional input feature, (c) grey zone and when using the expert’s diagnosis as an additional input feature.