چکیده مقاله
This study aimed to develop a machine learning model for early diagnosis and prediction of chronic kidney disease CKD By employing support vector machine SVM , random forest RF , decision tree DT , multi layer perceptron MLP , and k nearest neighbors KNN algorithms, combined with permutation feature importance for explainability, we sought to improve decision making in kidney disease management Our findings indicate that the DT algorithm outperformed others in predicting CKD, with specific gravity, serum creatinine, hemoglobin, and diabetes mellitus identified as key predictors This model holds potential for efficient patient screening and can be applied to more complex clinical data
کلیدواژهها
نویسندگان
Mohammad Khodabandeh
Department of Industrial Engineering & Management Systems line Amirkabir University of Technology Tehran olytechnic Tehran, Iran
Fatemeh Azarian
Department of Industrial Engineering Faculty of Engineering, Kharazmi Uneversity line Karaj, Iran
شیوه ارجاع
Khodabandeh, Mohammad and Azarian, Fatemeh,1403,Chronic Kidney Disease prediction using machine learning algorithms and XAI approach,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات دهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها28 شهریور 1403 · مشهد