Work

Review on Exploring Machine Learning Classifiers in the Diagnosis of Chronic Kidney Disease

Year: 2026

Type: article

Abstract: Chronic kidney disease (CKD) is a global healthcare issue that highlights the need for early identification for better quality of life for patients. This study evaluates various machine learning (ML) classifiers on datasets from UCI and self-collected sources in search of the best methods for CKD classification. This review examines commonly used ML models like support vector machine, K-nearest neighbor, naïve Bayes, decision trees, random forest, logistic regression and boosting-based ensemble methods. The results demonstrated the highest performance of ensemble methods. Despite these promising results, challenges related to model integration and interpretability still exist. Transparent models that are reliable and efficient are suitable for enhancement of clinical application(s). By overcoming these challenges, the work highlights importance of ML for CKD detection and treatment paving the way for artificial intelligence (AI)-driven healthcare solutions that are both effective and trustworthy.

Source: Sci

DOI: 10.3390/sci8040068

Authors: Sonam Bhandurge, Kuldeep Sambrekar, Rashmi Laxmikant Malghan, Karthik M C Rao

Institutions: KLS Gogte Institute of Technology, Visvesvaraya Technological University, Manipal Academy of Higher Education

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,448