Fuzzy twin kernel ridge regression classifiers for liver disorder detection
by Deepak Gupta; Barenya Bikash Hazarika; Parashjyoti Borah
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 24, No. 2, 2024

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.

Online publication date: Thu, 01-Feb-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com