Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

• Published in: Scientific reports Vol. 9; no. 1; p. 9583
• Main Authors: Elhoseny, Mohamed, Shankar, K., Uthayakumar, J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.07.2019; Nature Publishing Group
• Subjects: 59/36; 692/699/1585/104; 692/700/139; Algorithms; Artificial Intelligence; Benchmarking; Classification; Data Mining; Health care; Humanities and Social Sciences; Humans; Kidney diseases; Kidneys; Learning algorithms; Machine learning; multidisciplinary; Renal Insufficiency, Chronic - classification; Renal Insufficiency, Chronic - diagnosis; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-46074-2

At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.