On relationship analysis of health examination items using self-organizing maps

Abstract

In this paper, a method of analyzing relationships between items in specific health examination data is presented to cope with lifestyle-related diseases. The proposed method uses self-organizing maps, and focuses on twelve items such as hemoglobin A1c (HbA1c), glutamic-oxaloacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), gamma-glutamyl transpeptidase (γ-GPT), and triglyceride (TG). The proposed method picks up the data from the examination dataset according to the standard specified by some item values. The training data are then generated by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. The proposed method labels neurons in the map by using item values of training data as parameters, and examine the relationships between items in the examination data by observing clusters formed in the map. Experimental results reveal the relationships among HbA1c, GOT, GPT, γ-GTP and TG both in the unfavorable case of HbA1c deteriorating and in the favorable case of HbA1c being improved.