NDLI: Feature selection using Artificial Bee Colony for cardiovascular disease classification

Content Provider	IEEE Xplore Digital Library
Author	Subanya, B. Rajalaxmi, R.R.
Copyright Year	2014
Description	Author affiliation: Comput. Sci. & Eng., Kongu Eng. Coll., Erode, India (Subanya, B.; Rajalaxmi, R.R.)
Abstract	Machine learning techniques are widely used in medical decision support systems. Medical diagnosis helps to obtain different features representing the different variations of the disease. With the help of different diagnostic procedures, it is likely to have relevant, irrelevant and redundant features to represent a disease. Redundant features contribute to the wrong classification of the disease. Therefore, removing the redundant features reduces the size of the data and computation complexity. Identifying a good feature subset for effective classification is a non-trivial task. This requires an exhaustive search over the sample space of the dataset. The main objective of this paper is to use a metaheuristic algorithm to determine the optimal feature subset with improved classification accuracy in cardiovascular disease diagnosis. Swarm intelligence based Artificial Bee Colony (ABC) algorithm is used to find the best features in the disease identification. To evaluate the fitness of ABC, Support Vector Machine (SVM) classification is used. The performance of the proposed algorithm is validated against the Cleveland Heart disease dataset taken from the UCI machine learning repository. The experimental results show that, ABC-SVM performs better than Feature selection with reverse ranking. The results also show that, the proposed method obtained good classification accuracy with only seven features.
Starting Page	1
Ending Page	6
File Size	263652
Page Count	6
File Format	PDF
e-ISBN	9781479923205
DOI	10.1109/ECS.2014.6892729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Decision support systems Feature Selection Accuracy Medical services Electrocardiography Artificial Bee Colony Support Vector Machine Medical diagnostic imaging Optimization
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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