Assessment and Modeling of Routinely Used Biochemical Laboratory Data of Healthy Individuals and End‐Stage Renal Failure (ESRF) Patients by Three Different Chemometric Methods

Background In recent years, the use of biochemical markers has received increasing attention for purposes of risk assessment and clinical management in renal failure patients. Chemometric methods are often used in medical studies and there are already indications for their specific role as a tool of the medical statistics. Methods Three chemometric methods, discriminant analysis (DA), binary logistic regression analysis (BLRA), and cluster analysis (CA), were used for assessment and modeling of routinely used biochemical laboratory data of 18 parameters that were determined from 185 healthy individuals (HIs) and 173 end‐stage renal failure (ESRF) patients. Results The above‐mentioned chemometric methods were performed using the data set of 14 parameters since the rest 4 parameters did not present significant difference between healthy and patients. DA created a model using only ALB (Albumin), K (Potassium), TG (Triglyceride), and ALP (Alkaline phosphatase); BLRA model also used the above four parameters; CA classified all the cases into two clusters using the same four parameters and one more parameter, AST (aspartate aminotransferase). Conclusions This study provides models for assessment and modeling of routinely used biochemical laboratory data, finding groups of similarity among clinical tests usually determined on HIs and ESRF patients, contributing in data mining and reducing costs.