The Classificatied Prediction of Coronary Heart Disease Based on Patient Similarity Analysis

Abstract Objective:It is important for physicians' clinical decision support to classify the coronary heart disease (CHD).Customizing personalized predictive models for patients requires selecting a patient group from an existing medical database that most closely resembles the indexed patients. In this study,we introduce a new concept that using the patient similarity for the classification of patient with CHD.Materials and methods: We performed a structured representation of CHD patients. Obtain the multidimensional attribute distance matrix between patient pairs by calculating the multidimensional attribute distance of the patients. Predict similarity between patient pairs using machine learning (ML) models to predict clinical outcomes for indexed patients based on matched similar patients.Results:The new measure shows marked improvements over the traditional classification measures. LightGBM is the top-performing ML model. The best model achieved 88.52% accuracy.Conclusion:The medical applications of ML supported by similarity analytics represent a promising solution through which to reduce the physican workload to achieve the goal of “precision medicine”.