Machine Learning-Based Prediction System For Chronic Kidney Disease Using Associative Classification Technique

Abstract

Technological development, including machine learning, has a huge impact on health through an effective analysis of various chronic diseases for more accurate diagnosis and successful treatment. Kidney disease is a major chronic disease associated with aging, hypertension, and diabetes, affecting people 60 and over. Its major cause is the malfunctioning of the kidney in disposing toxins from the blood. This study analyzes chronic kidney disease using machine learning techniques based on a chronic kidney disease (CKD) dataset from the UCI machine learning data warehouse. CKD is detected using the Apriori association technique for 400 instances of chronic kidney patients with 10-fold-cross-validation testing, and the results are compared across a number of classification algorithms including ZeroR, OneR, naive Bayes, J48, and IBk (k-nearest-neighbor). The dataset is preprocessed by completing and normalizing missing data. The most relevant features are selected from the dataset for improved accuracy and reduced training time. The results for selected features of the dataset indicate 99% detection accuracy for CKD based on Apriori. The identified technique is further tested using four patient data samples to predict their CKD.