Optimization model research on major underlying factors in the subhealth condition evaluation in 1 City and 7 provinces in China

Background: The study aimed to analyze major underlying factors of the subhealth condition evaluation and find the optimization model. Methods: Selected 524 cases of health state and 453 cases of subhealth state from the research objects. A genetic algorithm was applied to discover the optimization model. The decision tree algorithm was used to find the main performance in the areas of physical, psychological, and social adaptation in the two populations which were the health state and the subhealth state. Conclusions: To establish the optimization model, the author would set up a curve-fitting equation between reduction of health self-assessment score (S-G1) and white blood cell (WBC) value in routine blood, so as to establish the relationship between S-G1 and WBC and found the approximate minimum solution of each equation. Besides, the author would analyze the differences between two populations in WBC examination to seize the difference. Revealed the differences of two populations in the areas of physical, psychological, and social adaptation by data mining and got the result that WBC of “the health state population” was higher than that of “the sub-health state population” in the index changes of WBC. The problem in social adaptation area of the subhealth state population was more serious in degree than the health state population; the reason was complex. If WBC of the health state population was near or below 5.5079, the object may be in the state of subhealth. However, if WBC was near or below 4.35, it is possible to enter the “morbid state.”