Predicting Duration of CKD Progression in Patients with Hypertension and Diabetes

Warangkana Khannara, Natthakan Iam-On, Tossapon Boongoen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


Renal failure is one of major medical diseases that is recently on the rise, especially in Thailand. In general, patients with hypertension and diabetes are at high risk of encountering this disorder. The medical cost for a large group of chronic-disease patients has been the burden not only to the local hospitals, but also the country as a whole. Without forward planning, the allocated budget may not cover the expense of increasing cases. This research aims to develop an intelligent model to predict the duration to progress kidney disease in those patients with hypertension and diabetes. As such, the predictive model can help physicians to acknowledge patients’ risk and set up a plan to prolong the progression duration, perhaps by modifying their behaviors. The methodology of data mining is employed for such cause, with records of 360 patients from Phan hospital’s database in Chiang Rai province between 2004 and 2014. Prior model generation, the underlying data has gone through conventional steps of data cleaning and preparation, such that the problems of incomplete and biased data are resolved. To explore the baseline of prediction performance, four classical classification techniques are exploited to create the desired model. These include decision tree, K-nearest neighbor, Naive Bayes, and Artificial Neural Networks. Based on 10-fold cross validation, the overall accuracy obtained with the aforementioned techniques is around 70% to 80%, with the highest of 86.7% being achieved by Artificial Neural Networks.
Original languageEnglish
Title of host publicationIntelligent and Evolutionary Systems
Subtitle of host publicationThe 19th Asia Pacific Symposium, IES 2015, Bangkok, Thailand, November 2015, Proceedings
PublisherSpringer Nature
Number of pages13
ISBN (Electronic)978-3-319-27000-5
ISBN (Print)978-3-319-26999-3, 978-3-319-38743-7
Publication statusPublished - 12 Nov 2015
Externally publishedYes

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


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