Feature selection and classification model construction on type 2 diabetic patient’s data

Authors:
Yue Huang;Paul McCullagh;Norman Black;Roy Harper
Affiliations:
School of Computing and Mathematics, Faculty of Engineering, University of Ulster, Jordanstown, Northern Ireland, UK;School of Computing and Mathematics, Faculty of Engineering, University of Ulster, Jordanstown, Northern Ireland, UK;School of Computing and Mathematics, Faculty of Engineering, University of Ulster, Jordanstown, Northern Ireland, UK;The Ulster Hospital, Dundonald, Belfast, Northern Ireland, UK
Venue:
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
Year:
2004

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Abstract

Diabetes is a disorder of the metabolism where the amount of glucose in the blood is too high because the body cannot produce or properly use insulin. In order to achieve more effective diabetes clinic management, data mining techniques have been applied to a patient database. In an attempt to improve the efficiency of data mining algorithms, a feature selection technique ReliefF is used with the data, which can rank the important attributes affecting Type 2 diabetes control. After selecting suitable attributes, classification techniques are applied to the data to predict how well the patients are controlling their condition. Preliminary results have been confirmed by the clinician and this provides optimism that data mining can be used to generate prediction models.