Comparative performance of extreme learning machine and Hammerstein-Weiner models for modelling the intestinal hyper-motility and secretory inhibitory effects of methanolic leaf extract of Combretumhypopilinum Diels (Combretaceae)

In this article, three data-driven approaches were explored, including two artificial intelligence (AI) based models namely; Extreme Learning Machine (ELM) and Hammerstein-Weiner (HW) models and a trivial linear model namely; multilinear regression (MLR). In this context, the models were developed using the onset of diarrhoea, the total number of wet faeces, total number of faeces, weight of intestinal content (g) and length of the small intestine (cm) as the independent variables. In contrast, distance travelled by charcoal meal (C) and volume of intestinal content (I) were considered as the dependent variables for the prediction of the intestinal hypermotility and secretory inhibitory effects of the methanol leaf extract of Combretum hypopilinum (MECH). Three different performance indicators including; mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE) and Root mean square error (RMSE) were employed in this research to calculate and determine the performance skills of the models. The obtained results indicated the reliable capability of ELM and HW over MLR model having NSE-values higher than 0.90 in both the calibration and verification stages. The results further demonstrated that, in terms of MAPE and RMSE, ELM and HW models showed higher performance efficiency than the MLR model. Even though HW outperformed the ELM and MLR models in the prediction of I. Whereas, ELM outperformed HW and MLR models in the prediction of C. Overall; the results proved the satisfactory ability of the AI-based models (HW and ELM) for modelling the Intestinal hypermotility and secretory inhibitory effects of MECH.