Rough information theory based approach to manage uncertainty in remote healthcare. (11th July 2022)

• Record Type: Journal Article
• Authors: Das, Sayan; Sil, Jaya
• Is Part Of: International journal of fuzzy computation and modelling. Volume 4:Number 1(2022)
• Page Start: 73
• Page End: 103
• Publication Date: 2022-07-11
• Subjects: uncertainty management -- rough set -- information theory -- remote healthcare -- conditional entropy -- fuzzy c-mean -- rule base -- decision system; Fuzzy mathematics -- Periodicals; Mathematical models -- Periodicals; Computer science -- Mathematics -- Periodicals; 004.01511313
• Languages: English
• ISSNs: 2052-353X

Abstract

Scarcity of doctors and lack of skilled manpower in rural India is a real challenge in diagnosing the people even for the common health-related problems such as flu, diarrhea etc. Due to a lack of domain knowledge and expertise, health assistants are unable to consistently categories patients as diseased or not. The paper aims to develop a rough information theoretic approach for diagnosing patients with minimum false cases by reducing uncertainty in health dataset. Knowledge granulation of rough set theory is used to partition the patients into positive region (PR, certainly diagnosed), and boundary region (BR, possibly diagnosed). Conditional entropy of patients in BR is measured, considering patients of PR and accordingly feature values of patients in BR are revised. The model is suitable for providing primary healthcare to the patients based on diagnosis; however, not substitutions of doctors and in emergency cases, patients are referred to the experts.