Machine Learning Techniques for Thyroid Disease Diagnosis: A Systematic Review

Razia, Shaik; Kumar, P. Siva; Rao, A. Srinivasa

doi:10.1007/978-3-030-38445-6_15

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…Numerous studies have been performed to date on various thyroid datasets ( Razia, Kumar & Rao, 2020 ). Currently, the UCI machine learning repository contains ten datasets.…”

Section: Related Studymentioning

confidence: 99%

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Islam

Haque

Miah

et al. 2022

PeerJ Computer Science

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Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine’s machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that the ANN Classifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.

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“…Numerous studies have been performed to date on various thyroid datasets ( Razia, Kumar & Rao, 2020 ). Currently, the UCI machine learning repository contains ten datasets.…”

Section: Related Studymentioning

confidence: 99%

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Islam

Haque

Miah

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…A set of parameters, such as the feature extraction method and classification approach, is used to compare the discussed techniques. A general survey is also proposed by Razia, Siva Kumar & Rao (2020) reporting an overview of various machine learning techniques in medicine.…”

Section: Related Workmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

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The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.

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“…Prabal et al (Razia et al, 2020) have proposed a robust algorithmic system to classifying thyroid texture. This calculation of selective features trains the traditional classifiers such as artificial neural network, random forest (RF), and SVM are based on auto regressive modelling.…”

Section: Related Workmentioning

confidence: 99%

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Sureshkumar¹,

Balasubramaniam²,

Ravi

et al. 2021

Expert Systems

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Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixedkernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

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Machine Learning Techniques for Thyroid Disease Diagnosis: A Systematic Review

Cited by 17 publications

References 11 publications

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

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