Chemometric assessment of clinical data for diabetes mellitus 2 type patients using self-organizing maps

The aim of this study was to offer an efficient and unsupervised strategy for medical data exploration to find relationships between clinical tests and major as well as concomitant syndromes of a specific disease. A large data set consisting of a group of 100 patients suffering diabetes mellitus type 2 disease characterized by more than 30 clinical parameters was explored using self-organizing maps (SOM) and classified by the use of non-hierarchical K-means algorithm implemented in the SOM. An attempt was made to correlate the classification results with the parameters of the metabolic syndrome. The classification result revealed 4 specific patterns of patients, each presented by respective discriminating parameters: (i) patients with well-controlled state of the disease, (ii) patients with disturbed kidney function, (iii) patients who do not keep the required medical treatment regime, and (iv) patients who neglect the disease. All patterns included respective number of patients with different clinical status and attitude to the health problem. Patients with a well-controlled state of the disease, although being chronic for a long period of time were characterized by very low density proteins and triglycerides levels as well as by the lowest levels for hemoglobin A(1c), total protein and creatine phosphokinase. Patients with disturbed kidney function were characterized by the highest averages of the uric acid, albumin, total protein and creatinine, and relatively low values for glucose. Patients who do not keep the required regime possessed the highest level of glucose along with maximal values for cholesterol and trombocytes. Patients who neglect the disease were characterized by high values of cholesterol and relatively high level of glucose, although the duration of the disease was the shortest as compared to the other groups of patients.