An Information-Dense Summary and Prediction Model Based on Machine Learning for the Atherosclerotic Heart Disease

Abstract

According to WHO, cardiovascular illnesses are the main cause of death globally, killing 17.9 million people every year. Machine learning analysis of patients’ Electronic Medical Records (EMR) data was helpful for disease risk prediction. Goal of our research was for constructing machine learning-based prediction model for atherosclerotic heart disease prediction. This research investigated machine learning algorithms, namely AdaBoost, Random Forest and Naive Bayes. The study began with data collection based on the electronic medical records from Harapan Kita National Heart Center patients from the period of 2016-2021. Data pre-processing produced 4691 records for dataset. Discussion with a medical professional was used to select the features predictions, which were thrombocyte, khermchc, erythrocyte, hematocrit, hermch, hemoglobin, age, leukocyte, and gender. The outcomes revealed AdaBoost reached the utmost ROC AUC score (68%), followed by Naive Bayes (66%) and Random Forest (56%). Subsequently we used Shapley Additive Explanations (SHAP) and beeswarm plot to reveal information-dense summary, to interpret the prediction result and to describe how each attribute affected the prediction model. We revealed that thrombocyte was the most important feature for the prediction model. This research contributed by paving the way a framework for predicting and improved the information-dense summary of arteriosclerosis heart disease prediction model.

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