title: Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.
abstract: This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes.
authors.affiliation: Department of Science & Technology Studies, Faculty of Science, University of Malaya, 50603 Kuala Lumpur Malaysia.
authors.forename: Mahnaz
authors.initials: M
authors.lastname: Kazemipoor
authors.affiliation: National Nutrition & Food Technology Research Institute, Faculty of Nutrition & Food Technology, Shahid Beheshti University of Medical Sciences, 1981619573 Tehran, Iran. Electronic address: m39faraji@yahoo.com.
authors.forename: Majid
authors.initials: M
authors.lastname: Hajifaraji
authors.affiliation: Department of Science & Technology Studies, Faculty of Science, University of Malaya, 50603 Kuala Lumpur Malaysia.
authors.forename: Che Wan Jasimah Bt Wan Mohamed
authors.initials: CW
authors.lastname: Radzi
authors.affiliation: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia; Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran. Electronic address: shamshirband@um.edu.my.
authors.forename: Shahaboddin
authors.initials: S
authors.lastname: Shamshirband
authors.affiliation: University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia.
authors.forename: Dalibor
authors.initials: D
authors.lastname: Petković
authors.affiliation: Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Miss Laiha
authors.initials: ML
authors.lastname: Mat Kiah
mesh_terms: D000328:Adult; D015992:Body Mass Index; D029974:Carum; D003198:Computer Simulation; D004311:Double-Blind Method; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008519:Medicine, Traditional; D009765:Obesity / Q000188:drug therapy* / Q000473:pathology; D008517:Phytotherapy; D010946:Plants, Medicinal; D060388:Support Vector Machine
keywords: Adaptive neuro-fuzzy system (ANFIS); Body fat percentage; Carum carvi; Estimation; Obesity management; Weight loss