NDLI: An efficient feature selection method for classification in health care systems using machine learning techniques

Content Provider	IEEE Xplore Digital Library
Author	Selvakuberan, K. Kayathiri, D. Harini, B. Devi, M.I.
Copyright Year	2011
Description	Author affiliation: Department of IT, Thiagarajar College of Engineering, Madurai (Kayathiri, D.; Harini, B.; Devi, M.I.) \|\| Innovation Labs (Web 2.0), TATA Consultancy Services, Chennai (Selvakuberan, K.)
Abstract	Data mining can be used for a large amount of applications. Among one is the health care systems. Usually, medical databases have large quantities of data about patients and their medical history. Analyzing this voluminous data manually is impossible. But this medical data contain very useful and valuable information which may save many lives if analyzed and utilized properly. Data mining technology is very effective for Health Care applications for identifying patterns and deriving useful information from these databases. Diabetes is one of the major causes of premature illness and death worldwide. In developing countries, less than half of people with diabetes are diagnosed. Without timely diagnoses and adequate treatment, complications and morbidity from diabetes rise exponentially. India has the world's largest diabetes population, followed by China with 43.2 million. This paper describes about the application of data mining techniques for the detection of diabetes in PIMA Indian Diabetes Dataset (PIDD). In this paper we propose a Feature Selection approach using a combination of Ranker Search method. The classification accuracy of 81% resulted from our approach proves to be higher when compared with previous results
Starting Page	223
Ending Page	226
File Size	190255
Page Count	4
File Format	PDF
ISBN	9781424486786
e-ISBN	9781424486793
DOI	10.1109/ICECTECH.2011.5941891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-08
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Databases Machine learning Diabetes Data mining Diseases
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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