M. Blazadonakis et al., DEEP ASSESSMENT OF MACHINE LEARNING TECHNIQUES USING PATIENT TREATMENT IN ACUTE ABDOMINAL-PAIN IN CHILDREN, Artificial intelligence in medicine, 8(6), 1996, pp. 527-542
Learning from patient records may aid knowledge acquisition and decisi
on making. Existing inductive machine learning (ML) systems such us Ne
wId, 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 al
ternative ML systems with each other and with respect to a novel appro
ach 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 in
formal expert assessment of learned knowledge.