Prediction Of Thyroid Disorders Using Advanced Machine Learning Techniques

Duggal, Priyanka; Shukla, Supriya

doi:10.1109/confluence47617.2020.9058102

Cited by 37 publications

(12 citation statements)

References 3 publications

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“… Duggal & Shukla (2020) perform feature selection and extraction before applying naive Bayes, Support Vector Machine, and Random Forest to identify hypothyroidism, hyperthyroidism, and euthyroid disease.…”

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%

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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

“…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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“…The problem statement is to estimate whether the patient has cancerous disease or not, with the help of a supervised machine learning algorithm [14]. Supervised machine learning algorithms such as logistic regression, naive Bayes, decision tree have used in this research to predict the cancer disease in patients [3].…”

Section: Problem Statementmentioning

confidence: 99%

Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

Verma

Popli

Kumar

et al. 2021

Communications in Computer and Information Science

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Healthcare diseases diagnoses projects often exceed schedules and budgets due to the complication and their large size. Project planning and estimation are exclusively challenging in large and globally distributed projects. Agile with machine learning techniques can overcome these challenges of healthcare projects largely. The purpose of this study is to estimate efforts in large-scale healthcare projects using user story point with machine learning technique. Will be using user stories to estimates the size of Healthcare projects. The templates of user stories: As a [user or destination], I [want to some goal] so that [the goal can be achieved]. According to Bill Wake a good user story must have INVEST acronym. To predict accuracy of machine learning algorithms such as Naïve Bayes, Decision Tree, Logistic Regression cancer patient dataset has used. Writing user stories for healthcare is an easy way to maintain the requirements for an agile project. Agile model in healthcare has used to maintain the effectiveness by adopting the frequently changing demand of the customers. This research paper work on cancer diseases and will provide accuracy, precision, recall using machine learning algorithms such as naïve Bayes, logistic regression, Decision Tree.

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Prediction Of Thyroid Disorders Using Advanced Machine Learning Techniques

Cited by 37 publications

References 3 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

Story Point Based Effort Estimation Model with Machine Learning Techniques in Healthcare

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