Classification and Association Rule Mining Technique for Predicting Chronic Kidney Disease

Ahmad Alaiad (), Hassan Najadat (), Belal Mohsen () and Khaled Balhaf ()
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Ahmad Alaiad: Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan
Hassan Najadat: Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan
Belal Mohsen: #x2020;Department of Computer Engineering, Jordan University of Science and Technology, Jordan
Khaled Balhaf: #x2020;Department of Computer Engineering, Jordan University of Science and Technology, Jordan

Journal of Information & Knowledge Management (JIKM), 2020, vol. 19, issue 01, 1-17

Abstract: Background and objective: Chronic kidney disease (CKD) is one of the deadly diseases that can affect a lot of vital organs in the human body such as heart, liver, and lungs. Many individuals might be at early stage of kidney disease and not have any signs, which might lead to a sudden death. Previous research showed that early prediction of CKD is very important in the medical field for physicians’ decision-making and patients’ health and life. To this end, constructing an efficient prediction system for CKD, which is the goal of this paper, often reduces medical errors and overall healthcare cost. Methods: Classification and association rule mining techniques were integrated and utilised to construct an efficient system for predicting and diagnosing CKD and its causes using weka and SPSS as platform environments. In particular, five classification algorithms, namely, naive Bayes, decision tree, support vector machine, K-nearest neighbour, and JRip were used to achieve the research goal. In addition, Apriori algorithm was used to discover strong relationship rules between attributes. The experiments were conducted on real medical dataset collected from hospitals and patient monitoring systems. Results: The experiments achieved high accuracy of 98.50% for K-nearest neighbour (KNN) classifier and achieved 96.00% when using classier based on association rule (JRip). Conclusions: We conclude by showing that applying integrative approach by combining classification algorithms and association rule mining can significantly improve the classification accuracy and be more useful for CKD prediction. This research has also several theoretical and practical implications for the medical field and healthcare industry.

Keywords: Chronic kidney disease; classification; association rule mining; KNN; predictive model (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0219649220400158

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