@article{MAKHILLJEAS201914517537,
    title = {A Literature Review on Diagnosing Thyroid Disease Through Artificial
Neural Network Techniques},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {5},
    pages = {1510-1517},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.1510.1517},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.1510.1517},
    author = {Vinod,V.P.,Rashmi and},
    keywords = {Thyroid disease,artificial neural network,appropriate,method,various,prediction},
    abstract = {In this study, researcher majorly focuses on usefulness and suitability of artificial neural network for
prediction of thyroid disease and respective role of neural network in medical diagnosis by past recorded data
is identified from a comprehensive literature review. Wherein, research contributions from 2013-2017 are
reviewed. It is found that different various architecture of Artificial Neural Network (ANN), Back Propagation
Network (BPN), Hybrid Back-Propagation Neural Network (BPNN), Data Mining Methods (DMM), Decision
Support System (DSS), Ranked Improved F-score Ordering (RIFO), Auto Associative Neural Network (AANN),
Multi-Layer Perception (MLP), Multivariate Bayesian Prediction Method (MBPM), Machine Learning Methods
(MLM) and Radical Basis Function Neural Network (RBFNN) are found to be proper and appropriately suitable.
In recent years, these architectures and methods are also found useful for prediction of so many other diseases.
The discussions of these architectures and their suitability, appropriateness for thyroid disease prediction is
presented through this review article and it would reveal the various methods and its benefits of diagnosing
thyroid diseases in a most appropriate and essential manner based on the nature and cause it affects on human
body. This review paper will certainly help various researchers to go through various research insights on
diagnosing thyroid diseases on a single point of contact and it help them to focus and choose the appropriate
method for diagnosing thyroid diseases.}
    }