Enhancing Chronic Kidney Disease Prediction with a Hybrid Hard Voting Classification Approach

Kavita Tabbassum; Waqas Ahmed Gadahi; Saima Shaikh; Shahnawaz Farhan Khahro

Authors

Kavita Tabbassum Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan.
Waqas Ahmed Gadahi Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan.
Saima Shaikh Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan.
Shahnawaz Farhan Khahro Energy Department, Government of Sindh, Pakistan.

Abstract

Chronic kidney disease (CKD) affects over 10% of the global population, equating to more than 800 million people. It is prevalent among older adults, women, minorities, and individuals with diabetes and hypertension. CKD also presents a significant health burden in low- and middle-income countries. The growing prevalence of CKD and its adverse impacts emphasize the urgent need for enhanced prevention and treatment strategies. Machine learning (ML), a prominent application of artificial intelligence, has made significant strides in healthcare research. The aim of this study is to design an ensemble method for more accurate prediction of CKD in patients. The proposed ensemble hard voting classifier integrates four machine learning algorithms—Support Vector Machine, Logistic Regression, K-Nearest Neighbors Classifier, and Random Forest Classifier. The model is trained on a dataset sourced from Kaggle, which includes data from 400 patients with 25 features.

Keywords: Chronic kidney disease, machine learning, ensemble method, hard voting classifier.