Based on data mining, analysis of diabetes disease risk prediction and diabetes medication patternAuthor(s): Ilaria Cavallari*
After heart disorders and cancerous tumours, diabetes mellitus is the second most prevalent disease. The number of diabetic patients is rising quickly and displaying a tendency of youth due to the ongoing acceleration of people’s living standards and life rhythms. According to a recent study, China has 114 million adult diabetics, a high prevalence rate, but low levels of awareness, medication adherence, and compliance. Diabetes can lead to a number of complications, including cardiovascular, cerebrovascular, and diabetic foot problems, which not only have a significant impact on the patient’s survival but also put a lot of strain on the patient’s family and society. These complications can be prevented if diabetes is treated and controlled early on. Therefore, controlling and preventing diabetes is a crucial method to conserve medical resources and lower medical expenses. In order to construct a prediction model of diabetes and investigate the law of medication for diabetic patients based on this analysis, we primarily read a lot of literature and gathered some significant theoretical knowledge to clarify the fundamental principles and methods of data mining. We also referred to the research findings of other scholars.