Deep assessment of machine learning techniques using patient treatment in acute abdominal pain in children.
Learning from patient records may aid knowledge acquisition and decision making. Existing inductive machine learning (ML) systems such us NewId, CN2, C4.5 and AQ15 learn from past case histories using symbolic and/or numeric values. These systems learn symbolic rules (IF... THEN like) which link an antecedent set of clinical factors to a consequent class or decision. This paper compares the learning performance of alternative ML systems with each other and with respect to a novel approach using logic minimization, called LML, to learn from data. Patient cases were taken from the archives of the Paediatric Surgery Clinic of the University Hospital of Crete, Heraklion, Greece. Comparison of ML system performance is based both on classification accuracy and on informal expert assessment of learned knowledge.
Blazadonakis
M
M
Institute of Computer Science, Foundation for Research and Technology, Hellas, Greece. mblaza@ics.forth.gr
Moustakis
V
V
Charissis
G
G
D000006:Abdomen, Acute / Q000145:classification / Q000601:surgery / Q000628:therapy*; D015746:Abdominal Pain / Q000145:classification / Q000601:surgery / Q000628:therapy*; D000465:Algorithms; D000704:Analysis of Variance; D001185:Artificial Intelligence; D002648:Child; D003657:Decision Making; D003663:Decision Trees; D005103:Expert Systems; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008128:Logic; D008297:Male; D008499:Medical Records; D010347:Patient Care Planning; D010351:Patient Discharge; D012984:Software; D013813:Therapy, Computer-Assisted