Towards Predicting the Grade of Peritonitis with a Mamdani-type Fuzzy Decision System: Application of Artificial Intelligence in Surgical Emergency Referrals

Abstract

Abstract Diagnostic healthcare has ambiguities due to the literal expression of symptoms, e.g., more, less, moderate, severe, and so on. Acute abdomen, associated with pain, vomiting, and fever of various grades, are commonly encountered in the surgical emergency department. Peritonitis is one of the commonest acute abdomens. It needs immediate medical attention as it can be life-threatening. Developing nations lack experienced clinicians and high-end investigation facilities, especially in the rural setup. Thus, referral of such cases to higher facilities is the mainstay procedure. A timely referral is a challenge. This paper has explained an intelligent referral system (IRS) by developing a fuzzy decision system (FDS) of Mamdani-type. Three symptoms - pain, vomiting, and fever, each having three grades, e.g., poor, average, and good are the inputs to the FDS, while the grade of peritonitis is the output as a low, medium, and high grade based on the fuzzy rule base obtained from an expert clinician. The efficacy of the FDS is tested and validated in three arbitrary cases.