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Hypergraph Based Feature Selection Technique for Medical Diagnosis

  • Systems-Level Quality Improvement
  • Published:
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Abstract

The impact of internet and information systems across various domains have resulted in substantial generation of multidimensional datasets. The use of data mining and knowledge discovery techniques to extract the original information contained in the multidimensional datasets play a significant role in the exploitation of complete benefit provided by them. The presence of large number of features in the high dimensional datasets incurs high computational cost in terms of computing power and time. Hence, feature selection technique has been commonly used to build robust machine learning models to select a subset of relevant features which projects the maximal information content of the original dataset. In this paper, a novel Rough Set based K – Helly feature selection technique (RSKHT) which hybridize Rough Set Theory (RST) and K – Helly property of hypergraph representation had been designed to identify the optimal feature subset or reduct for medical diagnostic applications. Experiments carried out using the medical datasets from the UCI repository proves the dominance of the RSKHT over other feature selection techniques with respect to the reduct size, classification accuracy and time complexity. The performance of the RSKHT had been validated using WEKA tool, which shows that RSKHT had been computationally attractive and flexible over massive datasets.

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Acknowledgments

The first and the fourth author thank the Department of Science and Technology, India for INSPIRE Fellowship (Grant No: DST/INSPIRE Fellowship/2013/963) and Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions (SR/FST/ETI-349/2013) for their financial support; The second author thanks the Tata Consultancy Services for their financial support; The third author thanks the Department of Science and Technology - Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions Government of India (SR/FST/MSI-107/2015) for their financial support. We would like to express our gratitude towards the unknown potential reviewers who have agreed to review this paper and provided valuable suggestions to improve the quality of the paper.

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Authors and Affiliations

  1. Centre for Information Super Highway (CISH), School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India

    Nivethitha Somu, M. R. Gauthama Raman & V. S. Shankar Sriram

  2. Department of Mathematics, SASTRA University, Thanjavur, Tamil Nadu, India

    Kannan Kirthivasan

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  1. Nivethitha Somu
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  2. M. R. Gauthama Raman
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  3. Kannan Kirthivasan
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  4. V. S. Shankar Sriram
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Correspondence to V. S. Shankar Sriram.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

Highlights

• This paper presents a novel Rough Set based K – Helly Property (RSKHT) feature selection technique to identify the predominant features in multidimensional medical datasets.

• RSKHT uses Rough Set Theory (RST) to obtain the possible reducts and K – Helly property of hypergraph to reveal the n – ary relations among the feature sets in the early stage of data representation.

• Optimal feature subset obtained from the RSKHT had been validated based on the reduct size, classifier’s accuracy and time complexity.

• The complexity of RSKHT technique had been reduced by the exploitation of the K – Helly property of the hypergraph.

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Somu, N., Raman, M.R.G., Kirthivasan, K. et al. Hypergraph Based Feature Selection Technique for Medical Diagnosis. J Med Syst 40, 239 (2016). https://doi.org/10.1007/s10916-016-0600-8

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