DEEP ASSESSMENT OF MACHINE LEARNING TECHNIQUES USING PATIENT TREATMENT IN ACUTE ABDOMINAL-PAIN IN CHILDREN

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.