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Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation
Jagan J, Dalkilç Y, Samui P.
Published in IGI Global
Pages: 224 - 251
The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.
About the journal
JournalAdvances in Environmental Engineering and Green Technologies Smart Cities as a Solution for Reducing Urban Waste and Pollution
PublisherIGI Global
Open Access0