# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 1 Introduction  
As a serious cardiovascular disease, the incidence and prevalence of heart failure are increasing all over the world. Due to the aging population and the prevalence of cardiovascular risk factors such as hypertension, coronary heart disease, and diabetes, the number of potential patients is huge [1]. The condition is hidden, and the early symptoms are mild, making it easy to be ignored or misdiagnosed. When the symptoms are obvious, the difficulty of treatment increases dramatically, the quality of life for patients decreases seriously, and the mortality rate is high. Undoubtedly, it has caused a heavy burden on the medical system and society [2, 3, 4]. In this case, it is self-evident that the research on heart failure prediction is important. Accurate prediction is helpful for medical workers to identify high-risk groups in advance. Intervention before the disease worsens, such as optimizing lifestyle, strictly controlling risk factors, and precise drug treatment,can effectively delay or stop the disease process and reduce the hospitalization rate and mortality rate of patients [5]. At present, there are many problems in the treatment of heart failure that need to be solved. Due to the concealment of symptoms of early heart failure and the lack of effective early diagnosis methods and accurate prediction models, it is difficult to accurately identify the high-risk individuals who may have heart failure in the initial stage of the disease. As a result, patients often miss the best opportunity for prevention and early intervention, and they can only receive passive treatment after the illness worsens, which has limited treatment effect and high medical expenses. In addition, some existing diagnostic methods may have some problems, such as insufficient accuracy, invasive damage to patients, complex operation, and high cost, meaning it can not meet the needs of large-scale population screening and early warning. At present, traditional diagnostic methods often rely on doctors' experience and limited detection indicators, which are subjective and carry the risk of a missed diagnosis. At the same time, although heart disease-related data are accumulating, the sample size is still insufficient. For example, the sample size of common data sets is only a few hundred to a thousand, so it is challenging to construct an accurate prediction model. The rise of machine learning technology provides a new method for this research field [6]. It demonstrates robust processing capabilities. It has the ability to discern underlying laws from complicated and limited heart disease data sources. However, these models still have some limitations in terms of accuracy and generalization. Based on the advantage of machine learning, this study deeply analyzes the related factors of heart disease and constructs a more accurate and reliable prediction model. It overcomes the limitations of traditional methods and the problem of small sample sizes, and provides strong support for the early diagnosis and intervention of heart disease. Instead of the previous machine learning models, bootstrap sampling and weighted fusion technology are innovatively adopted in this study. These methods enhance the model's adaptability to diverse data sets and, thus, improve the generalization ability of the model. Through bootstrap sampling, multiple different sub-sample sets can be generated from the original training data, thus increasing the diversity of data, enabling the model to learn a wider range of data feature patterns, and avoiding excessive dependence on specific data distribution. This method is helpful to improve the adaptability of the model to different data sets and, thus, improve the generalization ability. Now, the research on prediction of heart failure is developing vigorously from multiple dimensions. In terms of the risk factor assessment model [7], the traditional risk factor model determines factors such as age, disease history, living habits, established models, etc.