Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant

作者：Kazemipoor Mahnaz; Hajifaraji Majid; Radzi Che Wan Jasimah Bt Wan Mohamed; Shamshirband Shahaboddin^*; Petkovic Dalibor; Kiah Miss Laiha Mat

来源：Computer Methods and Programs in Biomedicine, 2015, 118(1): 69-76.

DOI：10.1016/j.cmpb.2014.10.006

• Obesity management
• Adaptive neuro-fuzzy system (ANFIS)
• Estimation
• Carum caroi
• Weight loss
• Body fat percentage

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 caroi) 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.

• 出版日期2015-1