Cadosa: A fuzzy expert system for differential diagnosis of obstructive sleep apnoea and related conditions

Abstract

Precise relationships between symptoms and diseases are rarely documented in the literature, and yet it is essential that the physician establishes a diagnostic label that will lead to the appropriate therapy. In obstructive sleep apnoea (a breathing disorder during sleep which is characterised by intermittent pauses in respiration) the borderline between normal and pathological is arbitrary. As such, fuzzy sets provide an intuitively appealing framework for representing the medical knowledge. In this paper, the expert system CADOSA for decision support in a hospital sleep clinic is presented. The medical knowledge in the system is stored in the form of fuzzy logical relationships between symptoms and diseases, between symptoms themselves and between symptom combinations and diseases. The symptoms present in the patient are confirmed by an interview, a physical examination, a questionnaire and laboratory tests, including an overnight oximetry study which measures oxygen saturation and heart rate. The fuzzy inference in the system is performed using the max-min compositional rule to calculate indication relations expressing occurrence and confirmability. These lead to confirmed and excluded diagnoses as well as diagnostic hypotheses. In an evaluation of CADOSA using 21 patients, the proportion diagnosed correctly as confirmed (excluded) obstructive sleep apnoea was 0.95 (1.00).