Big Data Investigation into the Causes and Treatment of Caries in Kindergarteners

Abstract

It has been thousands of years that tooth decay is a health problem among human beings (the Chu, 2000). The disease is like our common daily influenza. The aim of this paper is to use the heated topic big data analysis and its related statistical mathematics to predict the possible behavior behind kindergarten children tooth-care response — a predictive medicine for the prevention. Moreover, the paper also develops a thought experiment from the Bayes’ Decision tree. The aim is to determine some suitable strategies in the case of kindergarten tooth-caries — for regenerative medicine. During the research process, I have found that the Butterfly Effect can be a kind of predictive philosophy. Then I rationale the philosophy with Bayes Theory and map each outcome with the corresponding Domino effects (Heinrich Theorem). While in the middle part between these two well-known theorems, I insert random variables respectively as the connection. This event forms a completely new theory which can catch the chaos and dominos of the Butterfly Effects (philosophy) or the so-called Lorenz system. I propose the name should be the (HKLam’s) Net-Seizing Theory. When my theory is expressed in terms of mathematics (linear algebra) and statistics (random matrix and linear regression), one may use it in the prediction of human behavior etc. Furthermore, with the help of decision-making theory such as the Savage one, one can apply the machine learning technique to generate the necessary policy for handling the social problem which is just like the child’s tooth care shown in this research paper.