A distinct approach to diagnose Dengue Fever with the help of Soft Set Theory
This work addresses dengue fever diagnosis for medical practitioners, but it is incremental as it applies existing mathematical theories to a specific disease.
The paper tackled diagnosing dengue fever by developing a soft expert system based on soft set and fuzzy set theories to predict patient risk levels using variables like age and blood counts, achieving automated risk percentage predictions that circumvent medical imprecisions.
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.