Title: Development of rough-TOPSIS algorithm as hybrid MCDM and its implementation to predict diabetes

Authors: Shampa Sengupta; Debabrata Datta; S. Suman Rajest; P. Paramasivan; T. Shynu; R. Regin

Addresses: Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah-711 204, West Bengal, India ' Department of Information Technology, Heritage Institute of Technology Kolkata, West Bengal, India ' Department of Research and Development (R&D) and International Student Affairs (ISA), Dhaanish Ahmed College of Engineering, Chennai-601301, Tamil Nadu, India ' Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai-601301, Tamil Nadu, India ' Department of Biomedical Engineering, Agni College of Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai-89, Tamil Nadu, India

Abstract: In this work, an innovative approach of multi-criteria decision-making method guided by rough set theory is researched to predict diabetes. Diabetes is the root cause of various deadly diseases. Designing an expert diabetes prediction model can solve the health monitoring issue with preventive measures beforehand. The proposed work has mainly two phases. In the first phase, the ensemble classification method develops the classification model, and rough set theory is implemented as a feature selection technique. In the second phase, TOPSIS, a multi-criteria decision-making method, is implemented for optimising classification models. Ensemble classification methods used here in this work: Bagging, AdaBoost, M1, Logit Boost, attributed selected classifier, random subspace, and multi-class classifier. The technique for order preference by similarity to ideal solution, the so-called TOPSIS, a multi-criteria decision-making method, has been used to select the optimised prediction model. Experimental diabetes data are collected from the UCI repository. Results obtained for predicting diabetes agree with those obtained from clinical practitioners.

Keywords: rough set theory; RST; ensemble classification; diabetes prediction; TOPSIS; algorithm as hybrid; Adaboost; logit boost; multiclass classifier.

DOI: 10.1504/IJBRA.2023.135363

International Journal of Bioinformatics Research and Applications, 2023 Vol.19 No.4, pp.252 - 279

Received: 04 Jul 2023
Accepted: 19 Jul 2023
Published online: 06 Dec 2023 *

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