Intelligent disease identification based on discriminant analysis of clinical data

Abstract

Discriminant analysis was applied as a method of disease identification, using data obtained from blood analysis of several patients. The investigated compounds in human blood samples were organic compounds of clinical interest (glucose, triglycerides, cholesterol, creatinine and urea), inorganic compounds (Na, K, Ca, Mg and Fe) and enzymes (Lactate Dehydrogenase (LDH), Alanine Transaminase (ALT), Aspartate Aminotransferase (AST), Alkaline Phosphatase (ALP) and Gamma Glutamyltransferase (GGT)). According to their concentration level the following diseases have been selected for study: hydroelectric disorders, hepatic diseases, lipid disorders, diabetes and renal disorders. Some patients resulted to be healthy. Discriminant analysis was not only used for classifying the patients according to their disease but also for detecting the most important variables that discriminate between the groups. For example it has been found that the greatest contribution to the discriminatory power of the model is given by glucose (λ* = 0.263; F = 44). The obtained results confirm that clinical analysis combined with the multidimensional interpretation of data gives an interesting and very useful way of disease correlations, interpretations, problem solving and cost effectiveness.