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Can Fuzzy Interference Systems Be Used to Improve Disease Diagnosis?

This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases at a time when we are focused on contributing to the UNs Sustainable Development Goals (SDGs).

Professor of Computer Science and Information Technology, Dr. Adel Khelifi from the College of Engineering, with a team of international researchers, investigated the potential for fuzzy interference systems (FIS) to improve disease diagnosis by overcoming problems such as inaccurate data, missing data and the complexity of diagnosis when physicians are confronted with multiple interrelated complex variables. The team took an integrated approach to smart disease diagnosis using the Internet of Things (IoT) empowered by FIS to diagnose a range of diseases which was motivated by the ability of FIS to effectively analyze multiple uncertainties associated with disease diagnosis. They find that their proposed approach is capable of smartly and efficiently, via the IoTs, of diagnosing and tracking a range of diseases including COVID-19, Typhoid, Malaria, and Pneumonia.

Prof. Khelifi said “The results of our proposed method show that FIS could be utilized for the diagnosis of multiple diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases at a time when we are focused on contributing to the UNs Sustainable Development Goals (SDGs). Our study contributes to achieving SDG 3, ensure healthy lives and promote wellbeing for all ages, and SDG 9, which promotes innovation given the potential of our novel approach for new medical diagnostic tools”.

 

Reference: Alam, T.M., Shaukat, K., Khelifi, A., Khan, W.A., Raza, H.M.E., Idrees, M., Luo, S., Hameed, I.A. (2022) Disease diagnosis system using IoT empowered with fuzzy inference system, Computers, Materials and Continua  70 (3), pp. 5305-5319. https://doi.org/10.32604/cmc.2022.020344


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