# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 6 Conclusions and Future Work  
Heart disease is a major health problem in the world. Accurate heart disease prediction is of great significance for disease prevention, early intervention and medical resource allocation. With the continuous development of machine learning technology, the construction of heart disease prediction models by using data-driven methods has become a research hotspot, aiming to assist medical professionals to make more accurate diagnosis and decisions. This paper puts forward an effective and reliable heart disease classification model. By conducting an in-depth study and detailed comparison of diverse machine-learning models in the field of heart disease prediction, we explore methods to optimize the model's performance. Our ultimate goal is to offer more powerful support tools for the clinical diagnosis of heart disease. In this paper, a new strategy based on the LightGBM model and a weighted fusion sub-model using bootstrap sampling is proposed. The model is trained and optimized through cross-validation by traversing the multi-parameter combinations of LightGBM. Moreover,it is compared with common machine learning models such as KNN, SVM, decision trees, GBDT, and XGBoost. The bootstrap sampling operation carried out in the experimental part enables the model to better adapt to the diversity of sample distribution and data imbalance. Through targeted sample sampling, the model can learn effective patterns under different data subset characteristics, enhance its robustness, and perform better and relatively stably when facing different data sets. Weighted fusion can combine the advantages of different features or models and assign appropriate weights to each part. As a result, the final model can take advantage of LightGBM's own strengths. Additionally, by fusing other sub-models, it can further optimize performance and overcome the possible limitations of a single model. Finally, although the proposed fusion model still has some overfitting problems that need further improvement, it demonstrates excellent performance in aspects such as accuracy, precision, recall, and the F1 score. Moreover, it exhibits a favorable balance between true positive and false positive rates in the ROC curve comparison. Additionally, it has a considerably high AUC value. This proves the effectiveness of the model in heart disease data classification and that it can provide a powerful reference for diagnosis. Although some achievements have been made so far, some shortcomings still exist. In the next step, we will continue to conduct in-depth exploration in terms of both data and models and continuously enhance the model to boost its efficiency. Conceptualization, Y.W. ; methodology, Y.W. ; software, Y.W. ; validation, Y.W. ; formal analysis, H.C.; investigation, H.C.; resources, H.C.; writing--original draft preparation, Y.W. ; writing--review and editing, Y.W. and H.C.; visualization, Y.W. ; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the Declaration of Helsinki of 1975, which was revised in 2013. In this study, the data set ([[https://www.kaggle.com/fedesoriano/heart-failure-prediction](https://www.kaggle.com/fedesoriano/heart-failure-prediction)]([https://www.kaggle.com/fedesoriano/heart-failure-prediction](https://www.kaggle.com/fedesoriano/heart-failure-prediction)) (accessed on September 2021)) licensed by \"Open Data Commons Open Database License (ODBL) v1.0\" on Kaggle is used, and the source of the data has been given in the reference part of the paper. The original data of this data set come from the heart disease data set of UCI machine learning library ([[http://archive.ics.uci.edu/dataset/45/heart+disease](http://archive.ics.uci.edu/dataset/45/heart+disease)]([http://archive.ics.uci.edu/dataset/45/heart+disease](http://archive.ics.uci.edu/dataset/45/heart+disease)) (donated on 30 June 1988)).