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An Adaptive Soft Set Based Diagnostic Risk Prediction System

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Intelligent Systems Technologies and Applications (ISTA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 683))

Abstract

Recently, risk based prediction models in medical diagnostic systems gain wider significance in deciding most appropriate diagnostic treatments and for clinical usage. Prostate cancer is a disease which is difficult to diagnose and there are number of failure cases reported. Therefore, an effective and aggressive selection of multiple factors influence on the disease is required. In this paper, an adaptive soft set based diagnostic risk prediction system is presented with the implementation on prostate cancer. The system receives input parameters related to the disease and gives out the risk percentage of the patient. Soft sets are generated with the input parameters by fuzzification followed by rule generation. The risk percentage of the rules are individually calculated for Precision, Recall and F-Measure, that conclude on the best risk percentage based on the maximum area under the curve (AUC) in each case. This ensures to select the most influential risk parameters in treating the disease. Specificity and sensitivity of the test system yield 75.00% and 45.45% respectively.

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Author information

Authors and Affiliations

  1. School of Computer Sciences, Mahatma Gandhi University, Kottayam, India

    Terry Jacob Mathew

  2. IIITM-K, Technopark, Thiruvananthapuram, India

    Elizabeth Sherly

  3. BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, 37007, Salamanca, Spain

    José Carlos R. Alcantud

Authors
  1. Terry Jacob Mathew
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  2. Elizabeth Sherly
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  3. José Carlos R. Alcantud
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Corresponding author

Correspondence to Terry Jacob Mathew .

Editor information

Editors and Affiliations

  1. School of CS/IT, Indian Institute of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  3. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India

    Jayanta Mukhopadhyay

  4. Xiamen University, Xiamen, China

    Kuan-Ching Li

  5. Department of Electrical and Electronic, Nazarbayev University, Astana, Kazakhstan

    Alex Pappachen James

  6. Dipartimento di Ingegneria, Università degli Studi di Firenze, Firenze, Italy

    Stefano Berretti

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Mathew, T.J., Sherly, E., Alcantud, J.C.R. (2018). An Adaptive Soft Set Based Diagnostic Risk Prediction System. In: Thampi, S., Mitra, S., Mukhopadhyay, J., Li, KC., James, A., Berretti, S. (eds) Intelligent Systems Technologies and Applications. ISTA 2017. Advances in Intelligent Systems and Computing, vol 683. Springer, Cham. https://doi.org/10.1007/978-3-319-68385-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-68385-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68384-3

  • Online ISBN: 978-3-319-68385-0

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