# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 1 Introduction
The prediction model for lifelong risks can effectively identify high-risk individuals and carry out early intervention. In the detection of biomarkers, besides the commonly used brain natriuretic peptide markers [8], new markers such as neuropeptide Y [9] have been found to predict the death risk of patients with stable heart failure. In imaging technology, cardiac MRI [10] can evaluate the pressure inside the heart and predict heart failure, while echocardiography [11] can help find early asymptomatic heart failure by measuring the structural and functional parameters of the heart. The focus of gene detection and gene research is the relationship between gene polymorphism and hereditary cardiomyopathy and heart failure, which provides the basis for predicting the risk of high-risk family populations [12]. Machine learning and big data analysis use algorithms to mine multi-source data, extract features, and establish optimized prediction models, which are better than traditional models. These research results lay a solid foundation for the accurate prediction and effective prevention and treatment of heart failure [13; 14]. Although there has been a lot of research on heart failure, there are still obvious shortcomings. In regard to the aspect of prediction, most studies focus on the influence of single factor or a few factors on heart failure, lacking in-depth analysis and the integration of multi-dimensional and comprehensive factors into the prediction model. For example, some studies only pay attention to cardiac structural indexes or blood biochemical indexes, but they fail to comprehensively consider the comprehensive influence of many factors such as patients' clinical symptoms, lifestyle, family history, gene polymorphism, and so on. At the same time, the generality and accuracy of the existing forecasting models need to be further improved [15; 16]. Some models perform well in a specific population or a specific medical environment, but they are difficult to use widely in people of different regions, races, and age, and their ability to dynamically change the prediction results and long-term follow-up is weak. Considering the obvious limitations of existing commonly used heart disease data samples, the traditional single model method can easily fall into the overfitting dilemma when processing such a small sample of data. The reason for this situation is either that the complexity of the model does not match the richness of data characteristics, or that there are not enough diversified data to train, which leads to a significant decline in the accuracy of the prediction. Commonly used machine learning models, such as LightGBMs (light gradient boosting machines), SVMs (support vector machines), KNN (K-nearest neighbor), decision trees, GBDTs (gradient boosting decision trees), XGBoost (eXtreme Gradient Boosting), etc., are difficult to cope with many challenges brought by insufficient data. The purpose of this paper is to develop a new accurate prediction method of heart failure based on bootstrap sampling and the weighted fusion LightGBM model, aiming at solving the problems of traditional prediction methods in regard to accuracy, stability, and an insufficient ability to deal with unbalanced data, improving the efficiency of early diagnosis and risk assessment of heart failure, thus providing more reliable basis for clinical decision making and improving the prognosis management of patients. By bootstrap sampling, a number of different sub-sample sets can be derived from the original training data. This enriches the diversity of data, allowing the model to learn a wider range of data feature patterns, and prevents over-reliance on specific data distributions. This method is helpful to improve the adaptability of the model to different data sets and, thus, improve the generalization ability. The weighted fusion method is to synthesize the prediction results of multiple models based on different self-help sampling samples according to certain weights.