A Soft Computing Model for Physicians' Decision Process

TL;DR

This paper introduces a fuzzy set theory-based soft computing model to assist physicians in decision-making by integrating both crisp and fuzzy data from patient interviews, history, symptoms, and test results.

Contribution

It presents a novel fuzzy set theory application for designing a comprehensive physician decision model handling diverse data types.

Findings

Developed a fuzzy set-based mathematical model for medical decision support.

Enables integration of fuzzy and crisp data in clinical decision processes.

Provides a framework for physician aided evaluation of patient information.

Abstract

In this paper the author presents a kind of Soft Computing Technique, mainly an application of fuzzy set theory of Prof. Zadeh [16], on a problem of Medical Experts Systems. The choosen problem is on design of a physician's decision model which can take crisp as well as fuzzy data as input, unlike the traditional models. The author presents a mathematical model based on fuzzy set theory for physician aided evaluation of a complete representation of information emanating from the initial interview including patient past history, present symptoms, and signs observed upon physical examination and results of clinical and diagnostic tests.