An Empirical Model for Thyroid Disease Diagnosis Using Data Mining Techniques

Sidiq, Umar; Aaqib, Syed Mutahar

doi:10.1007/978-3-030-34515-0_61

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The patients of thyroid disease are increasing rapidly and suffering from various disease forms, including hyperthyroid, Hypothyroidism, thyroid nodule, cancer, and so on. 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.…”

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 aggregative features are defined specifically to observe the collective behavior of normal or nodisease instances. The computation of Mean for any features corresponding to Nodisease values is shown through Equation (13).…”

Section: Expanded Feature Set Generationmentioning

confidence: 99%

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Juneja

2022

Wireless Pers Commun

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Expanded and Filtered Features based ELM Model for Thyroid Disease Classification

Juneja¹

2021

Preprint

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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 multi-measure 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.

show abstract

An Empirical Model for Thyroid Disease Diagnosis Using Data Mining Techniques

Cited by 2 publications

References 9 publications

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Expanded and Filtered Features Based ELM Model for Thyroid Disease Classification

Expanded and Filtered Features based ELM Model for Thyroid Disease Classification

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