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 (Chu, 2018).
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. In the research, I have found that butterfly
effect can form a predictive philosophy. I rationale it with Bayes theory and map each outcome with the
corresponding domino effects (Heinrich theorem). While in the middle part, 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 linear transformation,
random matrix, and regression, one may use it in the prediction model (for the posterior distribution)
of human behavior, etc. If we construct a reversed Bayesian tree with all necessary posterior predicted
distributions (models), we may get the wanted forecasting prior (distribution) model and are a recursive
formalism or the Bayes filter. We may establish the corresponding regression tree, etc. Furthermore, with
the Savage theory, 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 – there
is a need to subsidy our kindergarten as early as possible for the result of best social return.