A Hybrid ANFIS Model for studying the impact of Zernike coefficients on Keratoconus utilizing BA optimization and FCM

Nowadays because of the complicated nature ofmodeling impact of effective variables in medical testing resultsand making strategy for better detecting diseases via newlydevelopeddata mining and machine learning techniques whichhave been applied to various fields to build intelligentinformation systems, many research papers has involved in thisissue. One of the severe and progressive eye diseases isKeratoconus (KC) which can affect the patient's ability to drive acar or read normal print. The Zernike coefficients are effectivevariables in KC and Visual Acuity (VA) in LogMAR form isregarded as a testing result for that. Therefore, the presentresearch adopts Bat-Inspired Algorithm optimization (BA)techniques to obtain appropriate parameter settings for Fuzzy CMeansclustering (FCM) and integrates the adaptive-networkbasedfuzzy inference system (ANFIS) model to construct amodel for predicting impact of Zernike coefficients in VA of aKeratoconic eye

کلیدواژه ها:

Adaptive network-based fuzzy inference system (ANFIS) ، Bat algorithm optimization ، Fuzzy C-Means Clustering Kearatoconus