Please use this identifier to cite or link to this item: http://localhost:80/xmlui/handle/123456789/8634
Title: Development of rough-TOPSIS algorithm as hybrid MCDM and its implementation to predict diabetes
Other Titles: (In) International Journal of Bioinformatics Research and Applications
Authors: Datta, Debabrata
Issue Date: 2023
Series/Report no.: Vol : 19;Issue : 4
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.
URI: http://localhost:80/xmlui/handle/123456789/8634
Appears in Collections:Information Technology (Publications)



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