A Study on Label TSH, T3, T4U, TT4, FTI in Hyperthyroidism and Hypothyroidism using Machine Learning Techniques

Shahid, Afzal Hussain; Singh, M. P.; Raj, Rahul Kumar; Suman, Rashmi; Jawaid, Drakhshan; Alam, M. Afshar

doi:10.1109/icces45898.2019.9002284

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…The authors of Shahid et al (2019) compare Random Forest, Support Vector Machine, and K-Nearest Neighbours on the UCI thyroid dataset, to discover the best performing algorithm, resulted to be Random Forest, in detecting hypo- and hyperthyroidism.…”

Section: Resultsmentioning

confidence: 99%

“…Looking at the public datasets, we observed that the most used dataset is the UCI one ( https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease ), exploited 27 times ( Duggal & Shukla, 2020 ; Shahid et al, 2019 ; Pan et al, 2016 ; Pavya & Srinivasan, 2017 ; Mahurkar & Gaikwad, 2017 ; Ahmed & Soomrani, 2016 ; Tyagi, Mehra & Saxena, 2018 ; Kumar, 2020 ; Pasha & Mohamed, 2020 ; Shen et al, 2016 ; Bentaiba-Lagrid et al, 2020 ; Raisinghani et al, 2019 ; Vivar et al, 2020 ; Li et al, 2019b ; Ma et al, 2018 ; Kour, Manhas & Sharma, 2020 ; Khan, 2021 ; Priyadharsini & Sasikala, 2022 ; Peya, Chumki & Zaman, 2021 ; Chaubey et al, 2021 ; Hosseinzadeh et al, 2021 ; Juneja, 2022 ; Kishor & Chakraborty, 2021 ; Islam et al, 2022 ; Saktheeswari & Balasubramanian, 2021 ; Chandel et al, 2016 ; Priya & Manavalan, 2018 ). The UCI dataset is characterized by 7,200 instances and 21 categorical and real attributes.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Aversano,

Bernardi,

Cimitile

et al. 2023

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…LDA was also adopted by [17], and based on his experiments, the hypothyroidism and hyperthyroidism classification accuracy rates reached 99.62%. Similarly, random forest (RF), support vector machine (SVM), and KNN were also applied separately with 98.5% accuracy rates obtained by the RF approach [25]. With the help of machine learning techniques, thyroid disorder can be efficiently detected [26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Machine Learning Techniques For Thyroid Disease Detectionmentioning

confidence: 99%

Multi-channel convolutional neural network architectures for thyroid cancer detection

et al. 2022

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Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians’ adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical framework, which comprises three adaptable multi-channel architectures that were positively evaluated using real-world data sets. The proposed architectures outperform existing statistical and machine learning techniques and reached a diagnostic accuracy rate of 0.989 with ultrasound images and 0.975 with computed tomography scans through the single input dual-channel architecture. Moreover, the patient-specific design was implemented for thyroid cancer detection and has obtained an accuracy of 0.95 for double inputs dual-channel architecture and 0.94 for four-channel architecture. Our evaluation suggests that ultrasound images and computed tomography (CT) scans yield comparable diagnostic results through computer-aided diagnosis applications. With ultrasound images obtained slightly higher results, CT, on the other hand, can achieve the patient-specific diagnostic design. Besides, with the proposed framework, clinicians can select the best fitting architecture when making decisions regarding a thyroid cancer diagnosis. The proposed framework also incorporates interpretable results as evidence, which potentially improves clinicians’ trust and hence their adoption of the computer-aided diagnosis techniques proposed with increased efficiency and accuracy.

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“…Similarly, Tyagi et al [5] estimated the probable risk of thyroid disease using conventional machine learning classifiers. Researchers in [6] evaluated the potential of machine learning classifiers such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Random Forest (RF) in the prediction of thyroid disease. Several other researchers collaborated to develop an effective machine learning-based approach for detecting thyroid disease [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

SSC: The novel self-stack ensemble model for thyroid disease prediction

2024

PLoS ONE

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Thyroid disease presents a significant health risk, lowering the quality of life and increasing treatment costs. The diagnosis of thyroid disease can be challenging, especially for inexperienced practitioners. Machine learning has been established as one of the methods for disease diagnosis based on previous studies. This research introduces a novel and more effective technique for predicting thyroid disease by utilizing machine learning methodologies, surpassing the performance of previous studies in this field. This study utilizes the UCI thyroid disease dataset, which consists of 9172 samples and 30 features, and exhibits a highly imbalanced target class distribution. However, machine learning algorithms trained on imbalanced thyroid disease data face challenges in reliably detecting minority data and disease. To address this issue, re-sampling is employed, which modifies the ratio between target classes to balance the data. In this study, the down-sampling approach is utilized to achieve a balanced distribution of target classes. A novel RF-based self-stacking classifier is presented in this research for efficient thyroid disease detection. The proposed approach demonstrates the ability to diagnose primary hypothyroidism, increased binding protein, compensated hypothyroidism, and concurrent non-thyroidal illness with an accuracy of 99.5%. The recommended model exhibits state-of-the-art performance, achieving 100% macro precision, 100% macro recall, and 100% macro F1-score. A thorough comparative assessment is conducted to demonstrate the viability of the proposed approach, including several machine learning classifiers, deep neural networks, and ensemble voting classifiers. The results of K-fold cross-validation provide further support for the efficacy of the proposed self-stacking classifier.

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A Study on Label TSH, T3, T4U, TT4, FTI in Hyperthyroidism and Hypothyroidism using Machine Learning Techniques

Cited by 13 publications

References 12 publications

A systematic review on artificial intelligence techniques for detecting thyroid diseases

A systematic review on artificial intelligence techniques for detecting thyroid diseases

Multi-channel convolutional neural network architectures for thyroid cancer detection

SSC: The novel self-stack ensemble model for thyroid disease prediction

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