Application of self-organizing maps to data classification and data prediction for female subjects with unhealthy-level visceral fat

In this paper, an application of self-organizing maps (SOM's) in classifying and predicting data of female subjects with unhealthy-level visceral fat is discussed. The proposed method chooses subjects fulfilling the standard specified by body mass index and abdominal circumference. It defines the class with subjects of which hemoglobin A1c (HbA1c) values and item values associated with a liver deteriorate, that with subjects having HbA1c and triglyceride values deteriorate, and that with remaining subjects. Normal SOM learning is conducted, using data generated from original values of twelve items such as HbA1c and glutamic-oxaloacetic. The constructed map consists of neurons with labels. The label of a winner determines the class of the presented unknown data. The prediction depends on the label of a winner for the presented unknown data, a set of original data that determine the label, and a set of next year's data of the subjects with the above original data. Experimental results reveal that the proposed method achieves the reasonably favorable accuracies in classifying data and in predicting HbA1c values.