Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions

Kalantari, Ali and Kamsin, Amirrudin and Shamshirband, Shahaboddin and Gani, Abdullah and Alinejad-Rokny, Hamid and Chronopoulos, Anthony T. (2018) Computational intelligence approaches for classification of medical data: State-of-the-art, future challenges and research directions. Neurocomputing, 276. pp. 2-22. ISSN 0925-2312

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Official URL: https://doi.org/10.1016/j.neucom.2017.01.126

Abstract

The explosive growth of data in volume, velocity and diversity that are produced by medical applications has contributed to abundance of big data. Current solutions for efficient data storage and management cannot fulfill the needs of heterogeneous data. Therefore, by applying computational intelligence (CI) approaches in medical data helps get better management, faster performance and higher level of accuracy in detection. This paper aims to investigate the state-of-the-art of computational intelligence approaches in medical data and to categorize the existing CI techniques, used in medical fields, as single and hybrid. In addition, the techniques and methodologies, their limitations and performances are presented in this study. The limitations are addressed as challenges to obtain a set of requirements for Computational Intelligence Medical Data (CIMD) in establishing an efficient CIMD architectural design. The results show that on the one hand Support Vector Machine (SVM) and Artificial Immune Recognition System (AIRS) as a single based computational intelligence approach were the best methods in medical applications. On the other hand, the hybridization of SVM with other methods such as SVM-Genetic Algorithm (SVM-GA), SVM-Artificial Immune System (SVM-AIS), SVM-AIRS and fuzzy support vector machine (FSVM) had great performances achieving better results in terms of accuracy, sensitivity and specificity.

Item Type: Article
Uncontrolled Keywords: Computational intelligence; Medical application; Big data; Detection; Ensemble algorithm
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 31 Jul 2019 02:16
Last Modified: 31 Jul 2019 02:16
URI: http://eprints.um.edu.my/id/eprint/21715

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