Predicting Coronary Heart Disease Using an Improved LightGBM Model: Performance Analysis and Comparison

Abstract

Coronary heart disease (CHD) is a dangerous condition that cannot be completely cured. Accurate detection of early coronary artery disease can assist physicians in treating patients. In this study, a prediction model called HY_OptGBM was proposed for predicting CHD by using the optimized LightGBM classifier. To optimize the LightGBM classifier, the hyperparameters of the LightGBM model were adjusted. In addition, its loss function was improved, and the model was trained using adjusted hyperparameters. In this study, the hyperparameters of the prediction model were optimized by applying the most advanced hyperparameter optimization framework (OPTUNA). The improved loss function is referred to as the focal loss (FL). In this study, a prediction model was evaluated by using CHD data from the Framingham Heart Institute. To evaluate the performance of the prediction model, various metrics, including precision, recall, F score, accuracy, MCC, sensitivity, specificity, and AUC, were used. The AUC value of the proposed model was 97.9%, which was better than that of other comparative models. The results demonstrate that the rate of early identification of CHD among the general population can be improved by utilizing the proposed method. This, in turn, could serve to mitigate the costs associated with the medical treatment of patients suffering from CHD.

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

“…A literature review and research articles indicate that LightGBM, a distributed gradient boosting framework known for its speed and e ciency, has been increasingly applied in healthcare and cardiovascular research. Its ability to handle large datasets e ciently makes it suitable for predicting cardiovascular events [30,31].…”

“…SVM, with its capacity to manage both linear and nonlinear relationships via a variety of kernels, is a robust alternative [24][25][26]. Moreover, tree-based ensemble models such as RF, XGBoost, and LightGBM have demonstrated their ability to capture complex data patterns [23,28,30,31,38]. In summary, RF stood out as the top performer, achieving the highest AUC and other metric values, underscoring its effectiveness in CHD prediction.…”

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“…LightGBM employs a histogram-based approach to find the best splits during tree construction, which drastically reduces computation time [61]. With its impressive scalability, customizable options, and ability to handle categorical features efficiently, LightGBM has become a preferred choice for many data scientists and machine learning practitioners seeking top-tier performance in diverse applications [21,62,63].…”

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“…Gestational diabetes can also develop during pregnancy [ 6 ]. Both hyperglycemia and hypoglycemia can cause complications, such as cardiovascular diseases, nephropathy, neuropathy, and retinopathy [ 7 , 8 ]. Traditional diabetes management includes pharmacotherapy, diet, exercise, and self-monitoring.…”