Title: Fuzzy twin kernel ridge regression classifiers for liver disorder detection

Authors: Deepak Gupta; Barenya Bikash Hazarika; Parashjyoti Borah

Addresses: National Institute of Technology, Arunachal Pradesh, India ' Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India ' Indian Institute of Information Technology, Guwahati, India

Abstract: The liver is a key organ in the human body that aids in the digestion of food, the elimination of toxins, and the storage of energy. Patients with liver disorders are on the rise all over the world. However, because the disorder's symptoms are unclear, it is difficult to diagnose it, which raises the disease's death rate. The study introduces novel fuzzy twin models for liver disease classification. In the first model, the membership is calculated based on the quadratic function called fuzzy twin kernel ridge regression-quadratic (FTKRR-Q). In the second model, we have calculated the fuzzy membership based on the centroid and named the model as fuzzy twin kernel ridge regression-centroid (FTKRR-C). For our research, the BUPA or liver disease dataset has been used from the UCI machine learning repository. Experimental results are compared with the twin support vector machine, kernel ridge regression classifier and twin kernel ridge regression classifier. The accuracy, sensitivity, F1-score, and Mathew's correlation coefficient are used to evaluate the suggested model's performance. Experiments are also carried out on some real-world benchmark datasets. The results reveal the applicability of the proposed models.

Keywords: twin kernel ridge regression; TKRR; fuzzy; liver disorder; biomedical data; classification.

DOI: 10.1504/IJBIDM.2024.136429

International Journal of Business Intelligence and Data Mining, 2024 Vol.24 No.2, pp.131 - 145

Received: 06 Dec 2021
Accepted: 31 Oct 2022
Published online: 01 Feb 2024 *

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