A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory

TL;DR

This paper introduces a soft expert system based on soft set and fuzzy set theories to accurately diagnose dengue fever and assess patient risk levels by handling medical imprecisions effectively.

Contribution

It presents a novel soft expert system that combines soft set and fuzzy set theories for improved dengue diagnosis and risk prediction.

Findings

Accurately predicts dengue risk levels using patient data.

Handles medical imprecisions effectively.

Demonstrates explicit risk percentage calculation.

Abstract

Mathematics has played a substantial role to revolutionize the medical science. Intelligent systems based on mathematical theories have proved to be efficient in diagnosing various diseases. In this paper, we used an expert system based on soft set theory and fuzzy set theory named as a soft expert system to diagnose tropical disease dengue. The objective to use soft expert system is to predict the risk level of a patient having dengue fever by using input variables like age, TLC, SGOT, platelets count and blood pressure. The proposed method explicitly demonstrates the exact percentage of the risk level of dengue fever automatically circumventing for all possible (medical) imprecisions.