Integrating explainable machine learning and user-centric model for diagnosing cardiovascular disease: A novel approach

Abstract

Conventional machine learning techniques in diagnosing cardiovascular disease have a limitation owing to the lack of interpretability of models. This study utilised an explainable machine learning approach to predict the likelihood of having CVD. Four machine learning models were employed for CVD diagnosis: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boost (XGB). Shapley Additive Explanations (SHAP) were used to provide reasoning for the models' predictions. Using these models and explanations, a user interface was developed to assist in diagnosing CVD. All four classification models demonstrated good accuracy in diagnosing CVD, with the KNN model showcasing the best performance (Accuracy: 71 %). SHAP provided the reasoning behind KNN predictions, and the predictive interface was developed by embedding these explanations to provide transparency behind the model's decisions.