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An Enhanced Diabetes Mellitus Prediction Using Feature Selection-Based Type-2 Fuzzy Model

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1688))

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Abstract

The diabetes mellitus has been known to be a serious illness and revered for its ability to cause high mortality rate. This disease is famous among both youth and adult for its existence in the human body, and very difficult to diagnose, thus, produces an under-diagnosis issue when clinicians try to pinpoint the precise symptoms for disease prediction. The majority of the currently used diagnostic and monitoring methods are designed on type 1 fuzzy logic or ontology, which, as a result of the inconsistent and ambiguous nature of the collected data, is unsatisfactory. Therefore, this paper proposes an enhanced feature selection-based enabled type-2 fuzzy logic (T2FL) model for the prediction of diabetes patients. The proposed model used Particle Swarm Optimization to select the most relevant features from the dataset so as to remove irrelevant features from the data, and T2FL technique was used for the classification of the disease. The model extract precise information and correctly conclude the result. The proposed technique utilizes T2FL to determine the membership values of the clinical information, and the decision-making mechanism properly processes the evidence derived from the crisp values. A comprehensive computer simulation using a diabetes dataset shows that the suggested strategy works noticeably better than the ones already in place based on type-1 fuzzy logic and ontology in terms of determination effectiveness.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, 240003, Kwara State, Nigeria

    Joseph Bamidele Awotunde

  2. Department of Computer Science and Communication, Østfold University College, Halden, Norway

    Sanjay Misra

  3. School of Industrial Management, Ho Chi Minh City University of Technology (VNU-HCM), HCMC, Vietnam

    Quoc Trung Pham

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  1. Joseph Bamidele Awotunde
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  2. Sanjay Misra
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  3. Quoc Trung Pham
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Correspondence to Joseph Bamidele Awotunde or Quoc Trung Pham .

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Editors and Affiliations

  1. Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Seoul, Korea (Republic of)

    Tai M. Chung

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Awotunde, J.B., Misra, S., Pham, Q.T. (2022). An Enhanced Diabetes Mellitus Prediction Using Feature Selection-Based Type-2 Fuzzy Model. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_43

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  • DOI: https://doi.org/10.1007/978-981-19-8069-5_43

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