Application of Improved Decision Tree C4.5 Algorithms in the Judgment of Diabetes Diagnostic Effectiveness

Abstract

C4.5 algorithm needs to scan data sets repeatedly when constructing decision tree, which leads to its inefficient operation. A C4.5 decision tree optimization algorithm based on data dimension reduction is proposed, which generates a more concise decision tree and improves the efficiency of the algorithm. At the same time, it eliminates the interference of redundant attributes and improves the accuracy of the algorithm prediction.The simulation results of the improved C4.5 algorithm and the traditional algorithm show that the accuracy and efficiency of the improved C4.5 algorithm are greatly improved.This algorithm is applied to the prediction system of diabetes diagnosis effect, which provides decision support for the diagnosis of diabetes in hospitals, realizes better precise medical treatment, and makes better use of social medical resources.Successful application of diabetes diagnosis and treatment results also provides technical ideas for large data decision-making in other fields.