The effect of ethanolic leaves extract of Hymenodictyon floribundun on inflammatory biomarkers: a data-driven approach

Abstract

Abstract Background Medicinal plants are used to manage pain and inflammatory disorders in traditional medicine. A scientific investigation could serve as a basis for the determination of molecular mechanisms of antinociceptive and antiinflammatory actions of herbal products. In this work, we used both artificial intelligence (AI) based models inform of adaptive neuro-fuzzy inference system and artificial neural network (ANN) as well as a linear model, namely; stepwise linear regression in modelling the performance of four different inflammatory biomarkers namely; interleukin (1L)-1β, 1L-6, tumour necrosis factor (TNF)-α and prostaglandin E2 (PGE2). This modelling was done using number of abdominal writes, the reaction time of paw licking in mice and paw oedema diameter as the input variables. Results Four different performance indices were employed, which are determination coefficient (DC), root mean squared error (RMSE), mean square error (MSE) and correlation co-efficient (CC). The results have shown the superiority of the AI-based models over the linear model. Conclusions The overall quantitative and visualized comparison of the results showed that adaptive neuro-fuzzy inference system outperformed the ANN and SWLR models in modelling the performance of the four inflammation biomarkers in both the calibration and verification phases.

Keywords

• Medicinal plants
• Antiinflammatory
• Inflammatory biomarkers
• Artificial intelligence
• Stepwise linear regression