Thyroid disease diagnosis: A survey

Shroff, Shanu; Pise, Siddhi; Chalekar, Pratiksha; Panicker, Suja

doi:10.1109/isco.2015.7282384

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…Various data mining and machine learning [13,14,15,16,17] algorithms were analyzed by the researchers for identifying the most significant, accurate and reliable method. The automated disease classification under the machine learning algorithm [18] is designed by the researchers to diagnose the disease based on the symptoms. Each stage of the machine learning algorithm was improved by the researchers to improve the accuracy of disease recognition.…”

Section: Related Workmentioning

confidence: 99%

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Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Juneja

2022

Wireless Pers Commun

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Thyroid disorder affects the regulation of various metabolic processes throughout the human body. The structural and functional disorders can affect the body as well as the brain. The computer-aided diagnosis system can identify the kind of Thyroid disease. One such machine learning framework is presented in this paper to recognize disease existence and type. In this paper, a fuzzy adaptive feature filtration, expansion, and again filtration based model is presented for generating the most relevant and contributing features. This two-level filtration model is processed in a controlled fuzzy-based multimeasure evaluation. At the first level, the composite-fuzzy measures are combined with expert's recommendations for identifying the ranked and relevant features. At the second level, the statistical computation based distance measure is applied for expanding the featureset. The fuzzification is applied to expanded featureset for transiting the continuous values to fuzzy-values. At this level, the fuzzy-based composite-measure is applied for selecting the most contributing and relevant features over the expanded dataset. This processing featureset is processed by the ELM classifier to predict the disease existence and class. Five experiments are conducted on two datasets for validating the performance and reliability of the proposed framework. The comparative analysis is conducted against the NaiveBayes, Decision Tree, Decision Forest, Random Tree, Multilevel Perceptron, and RBF Networks. The analysis outcome is taken in terms of accuracy, error, and relevancy based parameters. The proposed framework clams the significant gain in accuracy, relevancy, and reduction in the error rate.

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Section: Related Workmentioning

confidence: 99%

“…The additive N hidden nodes are the output vector. The activation functions us expressed through Equation (18).…”

Section: Thyroid Classificationmentioning

confidence: 99%

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Juneja

2022

Wireless Pers Commun

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“…In contrast, insufficient thyroid hormone secretion results in hypothyroidism. The thyroid gland creates the thyroid hormones levothyroxine also known as T4 and triiodothyronine, also referred to as T3 in [2,3].…”

Section: Introductionmentioning

confidence: 99%

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

et al. 2021

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Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain.

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ITD-ML: Improving Diagnosis Capabilities for Thyroid Disease Using Machine Learning

Dash,

Nayak,

Gupta

et al. 2024

Lecture Notes in Networks and Systems

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Thyroid disease diagnosis: A survey

Cited by 10 publications

References 23 publications

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

ITD-ML: Improving Diagnosis Capabilities for Thyroid Disease Using Machine Learning

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