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The purpose of this expert system is to assess a predisposition to bleeding in a patient undergoing a tonsillectomy and/or adenoidectomy, as may occur with patients who have certain blood conditions such as hemophilia, von Willebrand's disease, and platelet function defects. This objective is achieved by establishing a correlation between the patients' responses to a medical questionnaire and the relative quantities of blood lost during their operations.

Three major modules constitute the system: the automated questionnaire to be completed by the patient, the expert system proper, and the patient database. The questionnaire is divided into fourteen sections on topics such as the occurrence of bruises and positive familial history. The expert system takes the responses to the questionnaire and determines risk categories for patients undergoing the operations. The patient database contains the patients' questionnaire responses, personal data and the risk assessments generated by the expert system.

Subsequent to the development of the expert system prototype, inductive learning techniques were examined to automatically classify patients as either normal or abnormal. Specifically, Quinlan's ID3 induction algorithm was used whereby a training set of patient data generates a classification tree whose leaf nodes correspond to the classification outcomes.

This paper presents the architecture of the hematology expert system, identifies some problems encountered during the knowledge engineering process and presents some statistical data pertaining to the accuracy of its risk assessments. It also offers a comparative review of the expert system and inductive learning methodologies.