Machine Learning Modelling of Effective Cumin (Cuminnum cyminum L.) Genotype for Antipyretic Property Based on Secondary Metabolites

Abstract

Cumin is a flowering plant that has been used as a spice in multicuisine. It has various health benefits such as weight loss, cholesterol control, anti-diabetes, and more. It also consists of dietary fibers, vitamin B, vitamin E, and minerals, especially iron and magnesium. The present study was conducted for the effective implementation of two tasks, the prediction of cumin genotype in which the inputs were total phenolic, flavonoid, and antioxidant content and the classification of anti-pyretic activity. MLP and other ML algorithm simulations were committed to executing the tasks of prediction and classification. The effectiveness of the proposed model was compared with the various classification and regression techniques like SVM, Naïve Bayes, KNN, Logistic Regression, and Decision Tree. Along with the mentioned task, this paper also exhibits the implementation of feature selection techniques like PCA in ML-based prediction and classification. It was found that MLP with PCA has outperformed other algorithms.