A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the
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A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things Mehdi Hosseinzadeh1,2 · Omed Hassan Ahmed3 · Marwan Yassin Ghafour4 · Fatemeh Safara5 · Hawkar kamaran hama6 · Saqib Ali7 · Bay Vo8 · Hsiu‑Sen Chiang9
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Medical information systems such as Internet of Medical Things (IoMT) are gained special attention over recent years. X-ray and MRI images are important sources of information to be examined for a particular type of anomalies. Reports based on the images and laboratory examination results could be mined with machine learning techniques as well. Thyroid disease diagnosis is an important capability of medical information systems. The main objective of this study is to improve the diagnosis accuracy of thyroid diseases from semantic reports and examination results using artificial neural network (ANN) in IoMT systems. In order to improve generalization and avoid over-fitting of ANN during the training process, a set of multiple multilayer perceptron (MMLP) neural network with the back-propagation error ability is proposed in this paper. Moreover, an adaptive learning rate algorithm is used to deal with the slow convergence and the local minima problem of the back-propagation error algorithm. The proposed MMLP significantly increased the overall accuracy of thyroid disease classification. With MMLP with a set of 6 networks, an improvement of 0.7% accuracy is achieved compared to a single network. In addition, comparing to the standard back-propagation, by using an adaptive learning rate algorithm in the proposed MMLP, an improvement of 4.6% accuracy and the final accuracy of 99% have been obtained in IoMT systems. The proposed MMLP is compared to recent researches reported for thyroid disease diagnosis, and its superiority is shown. Keywords Internet of medical things · Multiple multilayer perceptron · Adaptive learning rate · Back-propagation · Thyroid disease · Artificial neural network
* Bay Vo [email protected] Extended author information available on the last page of the article
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1 Introduction Employing as Internet of Medical Things (IoMT) is on the rise in recent years. Diagnosis of thyroid disease is an important capability of medical information systems recently, because of the impact of thyroid on other human body organs [1]. The main objective of this study is to improve the diagnosis of thyroid diseases from the data collected from reports and examination results in IoMT. Artificial neural network (ANN) is one of the widely used machine learning techniques, in particular for medical systems. Both examination results and reports on medical images are the valuable sources of information to be mined by ANN for diagnosis purposes. In an ANN, the back-propagation error is a supervised learning algorithm that employs a gradient descent method. It is one of the most commonly used algori
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