Machine Learning and Multiple Regression-Based Study of the Medicinal Properties of Food

Abstract

We study the relationship between food ingredients and hot and cold properties based on the idea that "medicine and food have the same origin". Firstly, we classify foods with known hot and cold properties as typical representatives of flat, warm and cold properties, and use various machine learning algorithms to classify the typical food representatives. In order to further improve the reliability and accuracy of the classification, we applied Bayesian optimization to the SVM and the SVM was able to classify the typical food representatives again with an accuracy of 96.53%. Based on the above findings, we further analysed which chemical components played a key role in the hot and cold properties. We then applied multivariate logistic regression for quantitative analysis, using stepwise forward regression to minimise complete multicollinearity and OLS + robust standard errors to eliminate the effect of heteroscedasticity. Based on the analysis of the coefficients of the regression equations and the significance test results, the conclusions reached were consistent with the qualitative analysis, with the four main components of energy, water, minerals and fat playing a major role in the chilling and heating properties of food. In conjunction with the analysis in the full text, we make recommendations for the development of functional foods where heat and cold are the principles.