Efficient prediction of early-stage diabetes using XGBoost classifier with random forest feature selection technique

• Published in: Multimedia tools and applications Vol. 82; no. 22; pp. 34163 - 34181
• Main Author: Gündoğdu, Serdar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Classifiers; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diabetes; Diagnosis; Feature selection; Multimedia Information Systems; Special Purpose and Application-Based Systems; COVID-19; Feature selection; MLR; Random forest; Diabetes; XGBoost

Diabetes is one of the most common and serious diseases affecting human health. Early diagnosis and treatment are vital to prevent or delay complications related to diabetes. An automated diabetes detection system assists physicians in the early diagnosis of the disease and reduces complications by...