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An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

Author

Listed:
  • Ranjit Kaur

    (Lovely Professional University, Phagwara, India)

  • Kamaldeep Kaur

    (Lovely Professional University, Phagwara, India)

  • Aditya Khamparia

    (Lovely Professional University, Phagwara, India)

  • Divya Anand

    (Lovely Professional University, Phagwara, India)

Abstract

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.

Suggested Citation

  • Ranjit Kaur & Kamaldeep Kaur & Aditya Khamparia & Divya Anand, 2020. "An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(2), pages 22-37, April.
    • Handle: RePEc:igg:jhisi0:v:15:y:2020:i:2:p:22-37
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