Abstract
With the continuous development of the economic level, diabetes has become an increasingly concerning problem, and the incidence of diabetes is also rising, which has caused huge losses to human health and economic development. It is very important to build a self-improving prediction model with considerable accuracy. As the cause of diabetes is complex and there are differences between people, this paper selects a widely recognized medical diabetes dataset for research, which contains 768 samples and 9 variables. After grouping, the data set is simulated and predicted by the logistic regression model, and it is found that the accuracy reaches 77.9% without grouping. After grouping, the accuracy reached 79.5% and 86.9%, respectively. It was found that the logistic model could effectively predict diabetes. At the same time, reasonable classification of the diabetes data set could effectively improve the accuracy of the prediction model. The main risk factors were Diabetes Pedigree Function, BMI, pregnancy, and Glucose. This provides some new perspectives and ideas for future research.
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