# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 1 Introduction
Each model learns unique information on different subsets of data. Through reasonable weighted fusion, this scattered effective information can be fully integrated, and the deviation of a single model can be reduced, making the final prediction result more robust and accurate. Compared with the existing models, our method is expected to significantly improve the accuracy and generalization ability, and to provide more powerful support for accurate prediction and personalized treatment of heart failure, thus making new breakthroughs in this research field. Among the numerous sampling and ensemble learning methods, bootstrap sampling has obvious advantages over bagging and boosting. Although bagging also constructs several sub-models based on sampling, it puts more emphasis on reducing the variance of the model. In addition, bagging does not make use of data diversity as directly and fully as bootstrap sampling. Boosting trains several weak learners in series, with the emphasis on gradually correcting the mistakes of the previous learner. Its data enhancement and model diversity are different from bootstrap sampling, and in some cases, it may not be as effective as bootstrap sampling in mining potential information in the data. Bootstrap sampling extracts samples from the original data set and generates a series of different sub-data sets. This approach not only enhances the diversity of the data but also endows the models trained on these sub-data sets with varying characteristics and performances. Therefore, it has effectively improved the generalization ability of the models. In addition, the bootstrap sampling method is relatively simple and straightforward, and the calculation efficiency is higher. It does not need repeated iterative training like boosting; it only needs to train different sub-data sets independently each time and then fuse the results. This is very beneficial for large-scale data sets or limited computing resources, and many models with certain differences can be obtained in a short time. Bagging also has high computational efficiency, but when dealing with some complex models, the complexity of the model may be increased due to the large number of sub-models. Bootstrap sampling can control the complexity of the model to a certain extent and avoid overfitting. If there is noise or missing data in the original data set, bootstrap sampling enables the model to learn different noise patterns or missing value interpolation methods through multiple sampling, thus enhancing the robustness of the model. When dealing with these problems, bagging and boosting may require additional pretreatment steps or special designs. In addition, in order to explore all kinds of potential relationships and feature combinations in data more comprehensively, bootstrap sampling can achieve this goal better through different sub-sample sets, because it allows each sub-model to learn data from different angles, which is more in line with the needs of data mining and model generalization in this study. Compared with the traditional single model prediction method, the LightGBM model based on bootstrap sampling and weighted fusion significantly improved the accuracy of heart failure prediction. Through the bootstrap sampling technology, the distribution of different types of samples in the training process is effectively balanced, and the prediction bias of the model to a few types of heart failure patients caused by too many sample types is avoided. In terms of recall index, the predicted recall rate of patients with heart failure is 88.8%, which is 3 percentage points higher than that of the model without special treatment. This indicates that this model can identify actual heart failure patients more effectively, thus reducing the possibility of missed diagnoses. This model has good stability and explanatory ability.