Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms

Asif, Md. Asfi-Ar-Raihan; Nishat, Mirza Muntasir; Faisal, Fahim; Shikder, Md. Fahim; Udoy, Mahmudul Hasan; Dip, Rezuanur Rahman; Ahsan, Ragib

doi:10.1109/icece51571.2020.9393054

Cited by 47 publications

(5 citation statements)

References 9 publications

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“…The dataset was used to implement several machine learning algorithms and their confusion matrices were presented. After a detailed comparison of accuracy, precision, sensitivity, F1 score, ROC-AUC, it was conclusively determined that Multilayer Perceptron (MLPC) had the highest accuracy of 99.70% after hyperparameter optimization [6].…”

Section: IImentioning

confidence: 99%

Machine Learning Techniques for the Classification of Thyroid Disease

Vadar

2024

IJRASET

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In the healthcare system, dealing with a large amount of data is challenging. The techniques of machine learning are used in dealing with such data. As per NFHS – 5 statistics, thyroid diseases are increasing in India. Roughly 1 in 10 Indian adults suffer from a thyroid disorder. It has been expected that more than 42 million peoples suffer from thyroid disease. For the proper diagnosis of disease, it is vital to process the medical data accurately. This study classified thyroid disease cases into hyperthyroid, euthyroid, hypothyroid, and sick. This paper aims to inspect Logistic regression for multiclass categorizing the thyroid dataset. This logistic regression model is evaluated based on its precision, recall, F measure, ROC, RMS Error, and accuracy metrics. Based on the thyroid dataset, we find that logistic regression using the One-vs.-Rest heuristic is 85% accurate, while logistic regression using the multinomial is 86% accurate.

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Section: IImentioning

confidence: 99%

Machine Learning Techniques for the Classification of Thyroid Disease

Vadar

2024

IJRASET

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“…According to preliminary computational results, the proposed model identifies thyroid issues with an accuracy of 99.8% 37 . M Asif et al proposed that MLP was the most effective algorithm, achieving an accuracy of 99.70% 38 . Zhou et al used ten ML algorithms through thyroid surgery to demonstrate a corresponding model.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review

Saleh,

Othman

2024

Baghdad Sci.J

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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid disease predictions. A systematic literature review (SLR) strategy is used in this study to give a comprehensive overview of the existing literature on forecasting data on thyroid disease diagnosed using ML. This study includes 168 articles published between 2013 and 2022, gathered from high-quality journals and applied meta-analysis. The thyroid disease diagnoses (TDD) category, techniques, applications, and solutions were among the many elements considered and researched when reviewing the 41 articles of cited literature used in this research. According to our SLR, the current technique's actual application and efficacy are constrained by several outstanding issues associated with imbalance. In TDD, the technique of ML increases data-driven decision-making. In the Meta-analysis, 168 documents have been processed, and 41 documents on TDD are included for observation analysis. The limits of ML that are discussed in the discussion sections may guide the direction of future research. Regardless, this study predicts that ML-based thyroid disease detection with imbalanced data and other novel approaches may reveal numerous unrealised possibilities in the future

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“…Machine learning is on the trend in the health sector for a variety of reasons, including disease prediction, medical imaging diagnosis, and personalized medicine [22][23][24][25]. Numerous studies have been conducted on the use of data mining techniques to predict heart disease in recent times [26][27][28].…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset

Nishat

Faisal

Ratul

et al. 2022

Scientific Programming

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Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms.

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Computer Aided Diagnosis of Thyroid Disease Using Machine Learning Algorithms

Cited by 47 publications

References 9 publications

Machine Learning Techniques for the Classification of Thyroid Disease

Machine Learning Techniques for the Classification of Thyroid Disease

Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review

A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset

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