# Heart Failure Prediction Based on Bootstrap Sampling and Weighted Fusion LightGBM Model
## 2 Related Work  
The research status of heart failure prediction can be summarized as follows: risk factor evaluation, including the assessment of traditional and new factors; biomarker detection, including the improvement of traditional biomarkers and the exploration of new ones; the application of imaging technology, including cardiac MRI and echocardiography; genetic research, including gene polymorphism and genes related to hereditary cardiomyopathy; and machine learning, such as data mining, model construction, and big data analysis. At present, the research on risk factor evaluation models is more in-depth and comprehensive, including not only traditional risk factor models, but also lifelong risk prediction models and disease-specific correlation models. The traditional risk factor model, which includes common factors such as age, hypertension, diabetes, and coronary heart disease related to cardiovascular diseases, can preliminarily assess the risk of an individual developing heart failure. The long-term risk prediction model further extends the prediction time, and the first long-term risk prediction model of heart failure is proposed in [17]. Based on the current level of risk factors, such as body mass index, blood pressure, cholesterol,diabetes and smoking, this model can predict the risk of heart failure in the next 30 years, and help doctors to take preventive measures for high-risk groups, especially young people. In [18], the researchers developed the first race-specific and gender-specific risk prediction models for heart failure, which has preserved ejection fraction (HFpEF) and decreased ejection fraction (HFrEF). The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HfrEF model additionally included previous CAD. The risk factors included in the model have good discrimination and calibration ability. The researchers provided clinicians with the one-year death risk of hospitalized patients with heart failure through the heart failure risk model to evaluate whether this risk prediction can help clinicians improve treatment decisions, thus reducing the readmission rate or mortality rate of patients [19]. Furthermore, the use of the heart failure risk model is one of the promising methods to identify at-risk patients of compensatory heart failure (HF) in the vulnerable period (VP). In [20], the researchers believe that the structured approach, which combines remote life monitoring and risk stratification tools, is most effective in identifying the risk of patients with compensatory heart failure during the vulnerable period. The detection of biomarkers has always been an important means of predicting heart failure. Some studies have developed a biomarker-based predictive model for patients with stable coronary heart disease by evaluating and comparing the prognostic value of biomarkers and clinical variables [21]. Traditional plasma brain natriuretic peptide (BNP) and its precursor N-terminal pro-BNP are widely used in clinical diagnosis and severity evaluation of heart failure [22,23], but there is a problem of insufficient specificity. Moreover, in recent years, a series of novel biomarkers, including the promising neuropeptide Y, have been discovered, showing great potential in the field of heart failure prediction. These new biomarkers can be used as novel markers to predict the death risk of patients with stable heart failure [9]. In addition, the ratio of neutrophils to lymphocytes [24], small molecular RNA, and other markers [25] have also been found to be closely related to the occurrence, development, and prognosis of heart failure, providing more evidence for the early diagnosis, risk stratification, and prognosis evaluation of heart failure. Imaging technology plays an important role in the prediction of heart failure and is constantly developing and perfecting.