A Prediction Framework for Lifestyle-Related Disease Prediction Using Healthcare Data

With the improvement of living standards and changes in work habits caused by industrialization, the prevalence of diseases related to lifestyle is rising. In this context, the prevention of lifestyle-related diseases (LRDs) is extremely important. The majority of existing research exclusively concentrates on the prognosis of a particular LRD sickness, making it impossible for them to intelligently identify the important characteristics of the disease. Therefore, this study aims to propose a lifestyle-related disease prediction framework including three key components, called missing value module, feature selection module, and disease prediction module. The performance of the proposed framework is evaluated by using real medical data gathered during a hospital in Nanjing, China. The experiment shows that the proposed framework can automatically generate prediction ensemble models for specific LRDs diseases, and achieve good accurate performance.