Charles Bernando’s research while affiliated with Multimedia Nusantara University and other places

Objectives: The number of deaths from cardiovascular disease is projected to reach 23.3 million by 2030. As a contribution to preventing this phenomenon, this paper proposed a machine learning (ML) model to predict patients with arteriosclerotic heart disease (AHD). We also interpreted the prediction model results based on the ML approach and deployed modelagnostic ML methods to identify informative features and their interpretations. Methods: We used a hematology Electronic Health Record (EHR) with information on erythrocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, leukocytes, thrombocytes, age, and sex. To detect and predict AHD, we explored random forest (RF), XGBoost, and AdaBoost models. We examined the prediction model results based on the confusion matrix and accuracy measures. We used the Shapley Additive exPlanations (SHAP) framework to interpret the ML model and quantify the contribution of features to predictions. Results: Our study included data from 6,837 patients, with 4,702 records from patients diagnosed with AHD and 2,135 records from patients without an AHD diagnosis. AdaBoost outperformed RF and XGBoost, achieving an accuracy of 0.78, precision of 0.82, F1-score of 0.85, and recall of 0.88. According to the SHAP summary bar plot method, hemoglobin was the most important attribute for detecting and predicting AHD patients. The SHAP local interpretability bar plot revealed that hemoglobin and mean corpuscular hemoglobin concentration had positive impacts on AHD prediction based on a single observation. Conclusions: ML models based on real clinical data can be used to predict AHD.

Coronary heart disease (CHD), alternatively known as cardiovascular disease (CVD) is the number one cause of death in the world. Each person may develop different symptoms or no symptoms of CHD, which means that they do not know they have CHD until they experience a chest pain, a heart attack or cardiac arrest. These situations may be avoided if we are able to predict the early diagnosis of the heart disease and determine the most important risk factors associated with the disease. Currently, the accuracy of the prediction has remained inadequate and the most important risk factors have remained elusive. This research paper discusses many risk factors associated with the disease and presents the prediction models of coronary heart disease using supervised machine learning algorithms, namely Random Forest, XGBoost algorithms, as well as using Artificial Neural Network (ANN), a deep-learning-based algorithm. It uses the public dataset from the Cleveland database of UCI repository of coronary heart disease patients. The models are further optimized using Grid Search optimization algorithm. The results show that the Random Forest, XGBoost and ANN algorithms have accuracies of 81.11%, 82.22% and 86.67%, respectively. Equally important, the results of the feature importance signify the importance of maximum heart rate and nuclear stress test in predicting the early diagnosis of the disease.