An application of nonlinear canonical correlation analysis on medical data
In studies in the field of medicine, necessity to examine the relations between data sets composed of categorical variable groups is widely encountered. In this study, it was aimed to examine nonlinear canonical correlation analysis (OVERALS) method, which allows examination of relations among K number of categorical variable sets and structural similarities of the data set, and to discuss usefulness of the method in more comprehensive data sets obtained from studies carried out in the field of medicine in terms of practice and interpretation. Materials and methods: OVERALS method was applied to a part of data set obtained from a study carried out with diarrhea patients. In the study, 10 variables were divided into 3 groups, namely anamnesis, symptoms, and laboratory tests. In order to examine similarities and relationships among these 3 variable groups, OVERALS method was used and results were expressed with graphical presentations. Results: It was observed that OVERALS analysis allows more detailed presentation of data structure and relations among variable sets. Conclusions: OVERALS analysis proved to be a quite useful method in graphical expression and interpretation of data structure, revealing similarities and relational structures among multi-dimensional categorical variable sets, which are used often in the field of medicine and their comprehensive interpretation.
Key words: Alternating least squares algorithm (ALS), homogeneity analysis (HOMALS), multivariate analysis, nonlinear canonical correlation analysis (OVERALS)