Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model

Heart disease is a serious threat to human health. Accurate prediction is very important for disease prevention and treatment. The purpose of this study is to establish a more suitable prediction model of heart disease. Based on LightGBM, we have deeply integrated bootstrap sampling and weighting technology. We repeatedly use multiple parameters of LightGBM to perform bootstrap sampling on the original training set. Through this process, we not only obtain training sub-models for various training subsets but also mine rich data features. In the process of cross-validation, the weight coefficient for each sub-model is carefully determined by comprehensively evaluating multiple key performance indicators, including accuracy, precision, recall, and the F1 score. This approach effectively highlights the role of high-quality sub-models. In the test stage, each sub-model is weighted according to the weight corresponding to its specific parameter combination, and finally, an accurate prediction result is obtained. Compared with the traditional prediction model, the model shows better comprehensive performance in terms of various performance metrics such as accuracy, precision, recall, and F1 score, and also performs better in the paired t-test. Moreover, compared with the baseline model, the phenomenon of overfitting is obviously reduced. Although the model has not been verified by external data sets, it has, to a certain degree, significantly boosted its predictive ability, universality, and stability. Moreover, it has provided a feasible scheme for heart disease prediction, which is expected to play a crucial role in clinical auxiliary diagnosis and disease management. The research shows that this model has obvious advantages in heart disease prediction and can effectively enhance the accuracy and reliability of prediction.