A Study on Prediction and Assessment of Diabetes Mellitus Based on BP Neural Network and Decision Tree Model

Yuyuan Pan

doi:10.62051/tq9ygf93

Authors

Yuyuan Pan

DOI:

https://doi.org/10.62051/tq9ygf93

Keywords:

Correlation analysis; Lasso Regression; BP neural network model; Decision tree model.

Abstract

Diabetes mellitus is a metabolic disease characterised by the patient's blood glucose being chronically higher than the standard value. In this paper, we first used multiple feature screening technique to gradually screen 17 main indicators from 34 test indicators, subsequently, we used BP neural network model for glucose value prediction and trained the model by back propagation algorithm, and then we used decision tree model combined with the new test data to assess the risk of diabetes, by constructing a tree structure to classify and predict the presence or absence of the feature based on the risk of diabetes.

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