APPLYING MULTIPLE CASCADE GENERALIZATIONS TO PREDICTING OBESITY LEVELS
Issued Date
2026-05-01
Resource Type
ISSN
21852766
Scopus ID
2-s2.0-105035190216
Journal Title
Icic Express Letters Part B Applications
Volume
17
Issue
5
Start Page
493
End Page
500
Rights Holder(s)
SCOPUS
Bibliographic Citation
Icic Express Letters Part B Applications Vol.17 No.5 (2026) , 493-500
Suggested Citation
Meesri S., Amornsamankul S., Kraipeerapun P. APPLYING MULTIPLE CASCADE GENERALIZATIONS TO PREDICTING OBESITY LEVELS. Icic Express Letters Part B Applications Vol.17 No.5 (2026) , 493-500. 500. doi:10.24507/icicelb.17.05.493 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116216
Title
APPLYING MULTIPLE CASCADE GENERALIZATIONS TO PREDICTING OBESITY LEVELS
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Author's Affiliation
Corresponding Author(s)
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Abstract
Cascade generalization is used as the main structure for predicting obesity levels. Cascade generalization (CG) is a sequential combination of machines, where the result of the previous machine is applied to the current machine. Two sets of CG are generated, the first set receiving true data and the second set receiving false data, to predict true and false obesity levels, respectively. The results of both sets are combined to find the best prediction result. In this paper, two types of machines are used to generate CG, namely, a single neural network with multiple outputs and a binary neural network with multiple outputs. The accuracy results obtained using the proposed multiple CGs were found to be better than those obtained using individual machines combining the proposed technique.