## 1 Introduction  
The aging of society has become a crucial concern in many countries. It leads not only to care pressure in small families, but also increased in expenses for elderly welfare and medical care. A major aging-related health problem hip-fracture, which is common cause of disability and mortality in older people [1]. According to a survey, the number of hip fractures will increase to 6.26 million worldwide by 2050 [2]. Older patients with hip fracture impose a high care burden on their families and health-care services, which can result in numerous other negative consequences in these patients' lives. Higher mortality rates, decreased functional status, and reduced self-care ability in order people following hip fracture are well-discussed and well-known factors that have been discussed in relevant studies [3; 4; 5; 6]. Hip fractures in older people are always severe problems in public health, and many studies have explored this topic and analyzed the factors contributing to hip fracture [7; 8; 9; 10]. The trend of mortality in older patients with hip fracture is likely to change with improvements in care systems and surgical techniques [11; 12]. According to research [13], the long-term trend of first-year mortality in older patients with hip fracture has declined by 8.8% for women and 20.0% for men in the United States. In general, the most critical time after a hip fracture is the first year [12]. Three studies have indicated that the long-term trend of first-year mortality in older patients with hip fracture remained unchanged [14; 15; 16]. To improve the quality of treatment older patients with hip fracture, a thorough understanding of the underlying deterioration factors is necessary before clinical treatment. The aforementioned factors include comorbidities, patient demographics, and the physician. Making a decision about the type of specific intervention to administer is difficult before the factors related to the mortality of such patients are known. [PERSON] et al. published a 10-year trend analysis of hip fracture mortality. However, to our knowledge, no further survival-related analysis has been conducted for the first-year trend, no population-based study has reported the long-term trend in older patients with hip fracture, and no study has used a population-based approach to examine the association between the characteristics and mortality of older patients with hip fracture [17]. For multivariate survival analysis, semiparametric models have been used in related studies, among which one of the popular approaches is the Cox regression model. Even though the model is the most widely used survival analysis method in medical literature, it still has numerous drawbacks and limitations [18; 19]. The Cox proportional hazard method relies on assumptions that can easily be violated in the presence of time-dependent covariates. Moreover, the potential for covariate bias is considerable, and the method lacks individual predictions [20]. Even when fixed covariate values are used in the simpler Cox model to make individual predictions, this is computationally expensive [21]. Furthermore, the Cox proportional hazards method produces poor results with many input variables [22]. Multicollinear, continuous, and dichotomized data entered together in the model may produce unstable results [23]. Finally, this model may not be capable of modeling interaction terms when data are dispersed in multidimensional space with nonlinear interactions [22; 23]. With recent developments in data science technology, data classification methods have been used in many important research fields. Such methods have also been used to represent and develop some useful tools to support clinical diagnostic decisions and guidelines in medical care. Machine learning is a commonly used data-mining method, and it has been applied to analyze critical information hidden in medical databases. Many different machine-learning methods exist, and they are used for building predictive models for disease prognosis.