Inside the “black box”: Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis

Abstract

Background: Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures. Objective: To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction. Methods: Unlike conventional data-driven "black box" ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the "black box" at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias and highlight the important elements and features. Results: Experimental results on the benchmark Cleveland IHD dataset showed that the proposed clinical knowledge-enhanced ML pipeline overperformed state-of-the-art data-driven ML models, using even fewer features. Our model based on one-hot feature encoding and support vector machine achieved the best accuracy of 94.4% and sensitivity 95% by using only 7 discriminative attributes. Conclusion: We share insights and discuss the effectiveness of incorporating clinical input in machine learning to improve model performance, as well as addressing some practical issues such as data bias and interpretability. We hope this preliminary study on engaging clinical expertise to explore the "black box" would improve the trustworthiness of AI and its potential wider uptake in the medical field.

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