Random forest algorithm-based prediction method for diabetic retinopathy
10.19745/j.1003-8868.2024207
- VernacularTitle:基于随机森林算法的糖尿病性视网膜病变预测方法研究
- Author:
Ya-Bin ZHOU
1
;
Jian-Dun LI
;
Jing-Jing CHEN
;
Fu-Song JIANG
Author Information
1. 上海电机学院电子信息学院,上海 201306
- Keywords:
diabetic retinopathy;
diabetes;
random forest algorithm;
diabetes follow-up
- From:
Chinese Medical Equipment Journal
2024;45(11):8-14
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a random forest(RF)algorithm-based prediction method for diabetic retinopathy(DR)to solve the problems due to multi feature dimension for detecting diabetes follow-up data and outliers and noises in the values of some indicators in the disease samples.Methods Firstly,the feature selection of the follow-up dataset of diabetic patients from the endocrinology and metabolism departments of Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Kobe University Hospital in Japan was carried out using the Weka tool to screen out the features or subsets that were hightly correlated with DR;secondly,a model for assisting clinical diagnosis of DR was constructed based on feature subsets and RF algorithm;finally,model comparison experiments and ablation experiments were performed to validate the model performance and to determine which feature contributed the most to the model.Results The feature subset containing disease duration,glycosylated hemoglobin(HbA1c),thyroid stimulating hormone(TSH),total bilirubin(T-bilirubin),low density lipoprotein(LDL),serum creatinine(sCr)and albumin(ALB)correlated the most with RF.A model was constructed based on the above findings with RF algorithm,which behaved better than other models in terms of precision(0.92),accuracy(0.91),F1 score(0.91)and AUC(0.95).The results of ablation experiments showed that the disease duration contributed the most to the model,followed by albumin and serum creatinine,and then by LDL,total bilirubin,glycosylated hemoglobin and thyroid stimulating hormone.Conclusion The RF algorithm-based prediction method with high accuracy can be used for assisted diagnosis of DR.[Chinese Medical Equipment Journal,2024,45(11):8-14]
