To search, Click below search items.


All Published Papers Search Service


A Novel Approach for Thyroid Disease Identification Empowered with Fuzzy Logic


Amjad Hussain, Syed Anwar Hussnain, Abeer Fatima, Shahan Yamin Siddiqui, Anwar Saeed, Yousaf Saeed, Aiesha Ahmed, Muhammad Adnan Khan


Vol. 20  No. 1  pp. 173-186


In the proposed research, A Multi-layered Fuzzy Mamdani Inference System (ML-MFIS) is set to analyze the prevailing Thyroid Disease (TD) which is termed as a common Thyroid disorder which leads to different diseases. The Proposed Expert System (TDI-EFL-ES) based on the symptoms and tests, used for diagnosis of the thyroid disease. The propose Expert system has been designed for non-specialist people by providing skills like specialists to get accurate results. The Thyroid Disease Identification Empowered with Fuzzy Logic Expert System is based on two layers. Both layers show the input variables. In Layer-1, use six input variables that identified the condition of Thyroid. Then in Layer-II, more tests are done such as Stimulating the Thyroid hormone (STH), Triiod-othyronine (T3), Thyr-oxine (T4), Neck Ultrasound, Thyroid Stimulating Module (TSM) to determine the disease type whether it is Hyperthyroidism or Hypothyroidism. Hyperthyroidism is caused when thyroid releases too many hormones. Hypothyroidism is a common condition characterized by too little thyroid hormone. In this research, presents the analysis of the accurate results using proposed Thyroid Disease Identification Empowered with Fuzzy Logic with the help of medical specialists, collected from Sheikh Zaid Hospital, Lahore, Pakistan. TDI-EFL Expert system has achieved 85.33% accuracy in the diagnosis of Thyroid Disease.


TD, MFIS, ML-MFIS, TDI-EFL-ES, hypothyroidism, hyperthyroidism