On robust partial discriminant analysis as a decision-making tool with clinical and analytical chemical data.
Classification is one of the fundamental goals of science and is basic to the diagnosis of disease. Unfortunately, classifying objects (e.g., patients) on the basis of clinical and/or laboratory experimental observations into various groups can be difficult when the groups overlap or contain outlying points. Recently, Broffitt, Randles, and co-workers proposed a procedure, robust partial discriminant analysis (RPDA) for dealing with such problems, but testing of the procedure was limited to Monte Carlo simulation. In this study, RPDA was applied to real data, in order to compare its effectiveness with ordinary discriminant analysis, as well as to determine if RPDA was a suitable procedure to use to classify chemical compounds on the basis of experimental observations and as a tool in the diagnosis of disease (in particular, multiple sclerosis and thyrotoxicosis), with data based on experimental and clinical observations. The resulting RPDA classifications were an improvement over those obtained from ordinary discriminant analysis.
D000447:Aldehydes / Q000145:classification; D002264:Carboxylic Acids / Q000145:classification; D055598:Chemical Phenomena; D002621:Chemistry / Q000379:methods*; D002627:Chemistry, Physical; D002965:Classification / Q000379:methods*; D019937:Diagnostic Techniques and Procedures; D006801:Humans; D006980:Hyperthyroidism / Q000175:diagnosis; D007659:Ketones / Q000145:classification; D009103:Multiple Sclerosis / Q000175:diagnosis; D013223:Statistics as Topic
D000447:Aldehydes; D002264:Carboxylic Acids; D007659:Ketones