title: Automated extraction of decision rules for leptin dynamics--a rough sets approach.
abstract: A significant area in the field of medical informatics is concerned with the learning of medical models from low-level data. The goals of inducing models from data are twofold: analysis of the structure of the models so as to gain new insight into the unknown phenomena, and development of classifiers or outcome predictors for unseen cases. In this paper, we will employ approach based on the relation of indiscernibility and rough sets theory to study certain questions concerning the design of model based on if-then rules, from low-level data including 36 parameters, one of them leptin. To generate easy to read, interpret, and inspect model, we have used ROSETTA software system. The main goal of this work is to get new insight into phenomena of leptin levels while interplaying with other risk factors in obesity.
authors.affiliation: Technical Faculty "Mihajlo Pupin" Zrenjanin, Serbia.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Brtka
authors.forename: Edith
authors.initials: E
authors.lastname: Stokić
authors.forename: Biljana
authors.initials: B
authors.lastname: Srdić
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D020738:Leptin / Q000097:blood*; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000097:blood* / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D015415:Biomarkers; D020738:Leptin