# A Cardiovascular Disease Risk Score Model Based on High Contribution Characteristics
## 1 Introduction  
CVD is a series of diseases involving the circulatory system, including angina pectoris, myocardial infarction, coronary heart disease, heart failure, arrhythmia and else, which is generally related to atherosclerosis [1]. According to the 2019 Global Burden of Disease Report statistics [2], the number of CVD patients has steadily increased, reaching 523 million in 2019, of which 18.6 million died, accounting for one-third of the total deaths [3]. Studies have found that cardiovascular disease is often associated with a number of factors, including age, smoking, high blood pressure, diabetes, blood lipid levels, chronic kidney disease, alcohol consumption, insufficient physical activity, unreasonable diet, family history and so on [4]. Therefore, we need to explore the relationship between risk factors and diseases, and use data analysis as the theoretical support to find its inherent laws to achieve accurate prediction of disease occurrence. Because of the high fatality rate of cardiovascular disease, many institutions have carried out prospective studies of cardiovascular disease. Typical representa tives include the method recommended by the American Heart Association/American College of Cardiology (ACC/AHA) [5], Coronary risk assessment study in European system [6], UK disease risk study based on QResearch database [7, 8], cardiovascular disease cohort study in many provinces and cities in China [9], etc. These institutions have launched risk assessment tools for cardiovascular disease, coronary heart disease, stroke, heart failure and other diseases. However, existing CVD risk assessment models have an implicit assumption that each risk factor has a linear relationship with the probability of CVD [10]. Such anassumption may oversimplify the relationship because it includes a large number of Risk factors with nonlinear interactions [11]. Because of their restrictive modelling assumptions and limited number of predictors, the existing algorithms usually fail to predict CVD risk correctly [12], especially for certain subgroups [13]. Recent years have seen remarkable advances in the application of machine learning (ML) in healthcare and medical research, thanks to high-performance computers [14]. The machine learning model can establish a complex nonlinear relationship between risk factors and diseases by minimizing the error between the predicted results and the real results [15]. This kind of machine learning algorithm has good prediction ability and can also detect potentially risk variables, such as early screening imaging tools or medical therapy (e.g., anti-platelet therapy) [16]. In the field of cardiovascular disease prediction, Typical of this is [PERSON] et al. [17] who used non-linear SVC to predict CVD risk of patients in the US and Italy, [PERSON] et al. [18] used 8 indicators to establish a risk assessment model based on Support Vector Machines (SVM) to predict the sensitivity and specificity of CVD [19]. Although they have obtained good model accuracy, the number of indicators chosen can cause more burden to the patient when actually applied to the clinical setting. Existing models can make predictions but do not provide an accurate score of risk of disease. Therefore, this paper aims to establish a CVD risk scoring model with less features and high accuracy. In this work, we propose XGBH model, which is based on a small number of features to achieve high-precision prediction. The key step of XGBH is to introduce histogram algorithm, which can effectively reduce the number of features in the sample and save memory space. To supplement previous studies, we simulated the performance of the real world on 14,832 retrospective data on cardiovascular patients and the kaggle competition cardiovascular disease dataset. At the same time, the XGBH model is compared with four previous machine learning models, and the superiority of the model is proved.