The Application of an Evolutionary Model using Fuzzy Logic on Health Literacy Data

Aims: Health literacy (HL) is the main factor shows health literate level of people in a certain society. Discovering and understanding affective factors on HL level could lead experts to improve these factors in the target community. This study aimed to Health Literacy classification of population and find a major component with data mining approaches. Instruments and Methods: In this paper, we have acquired more details about major factors on the health literacy level of target society by assessing evolutionary methods. We benefit of Particle Swarm Optimization (PSO) and KNN and fuzzy KNN algorithm for classification and use wrapper technique for feature selection by our model. Feature selection are done as weighted features and selects the most effective features of health literacy. Our proposed model evaluates a data set of Health Literacy by two classifiers with/without fuzzy logic. Applied data set is a real data gathered from a descriptive-analytic cross-sectional study on adult population include ۲۱۳۳ record with ۷۴ attributes in ۲۰۱۶ at South Khorasan province. We have gained effective factors on HL level of the population according to regions and total population without using any statistical analysis tools with the lowest human interference by an evolutionary method. Findings: Proposed model have found effective factors on the health literacy level of population in South Khorasan province. Results are obtained ۹۲.۰۲% accuracy for the total population and ۹۷.۹۹% for regions population. Conclusion: Simulations demonstrate the evolutionary method is a suitable way for extracting results from health data sets and also shows the superiority of the proposed method.

Keywords:

Health Literacy , Data Mining , Machine Learning , Fuzzy Logic