Prediction of thyroid Disease Using Data Mining Techniques

Begum, Amina; Parkavi, A.

doi:10.1109/icaccs.2019.8728320

Cited by 42 publications

(18 citation statements)

References 1 publication

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“…Electronic health records have provided access to vast clinical data, the application of data mining techniques has helped transform this data information into valuable knowledge for making health care decisions [53]. Also, data mining algorithms have been used on health record data sets to analyze factors contributing to autoimmune diseases such as those associated with thyroid disease [54].…”

Section: Application Of Data Science In the Treatment Of Autoimmune Thyroid Diseasesmentioning

confidence: 99%

“…Patient data collected from healthcare organizations is useful for accessing the risk factors analysis of diseases such as autoimmune thyroid disease. Classification algorithms is one of the most important applications in the data mining field, which can be used to make decisions in many real-world problems [51,54]. A recent study uses 34 unique clinical data (variables) such as patients' age at the time of diagnosis and information regarding lymph nodes to build novel classifiers that distinguish patients who probably live for over ten years since diagnosis from those who did not survive at least five years.…”

Section: Application Of Data Science In the Diagnosis Of Autoimmune Thyroid Diseases 41 Application Of Data Science In The Diagnosis Of Gmentioning

confidence: 99%

“…Hence, exploring clinical dataset with data science has helped in the prognosis of autoimmune thyroid disease. Some of the recently proposed algorithms with high accuracy are Expert System for Thyroid Disease Diagnosis (ESTDD), this is an expert system that diagnose thyroid diseases via neuro fuzzy rules with about 95% accuracy [54,83].…”

Section: Importance Of Data Science In Thyroid Diseasesmentioning

confidence: 99%

See 2 more Smart Citations

Application of Data Science Approaches to Investigate Autoimmune Thyroid Disease in Precision Medicine

Ajayi¹,

Adebayo²,

Adebayo³

et al. 2022

Hypothyroidism - New Aspects of an Old Disease

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In recent times, the application of artificial intelligence in facilitating, capturing, and restructuring Big data has transformed the accuracy of diagnosis and treatment of diseases, a field known as precision medicine. Big data has been established in various domains of medicine for example, artificial intelligence has found its way into immunology termed as immunoinformatics. There is evidence that precision medicine tools have made an effort to accurately detect, profile, and suggest treatment regimens for thyroid dysfunction using Big data such as imaging and genetic sequences. In addition, the accumulation of data on polymorphisms, autoimmune thyroid disease, and genetic data related to environmental factors has occurred over time resulting in drastic development of clinical autoimmune thyroid disease study. This review emphasized how genetic data plays a vital role in diagnosing and treating diseases related to autoimmune thyroid disease like Graves’ disease, subtle subclinical thyroid dysfunctions, Hashimoto’s thyroiditis, and hypothyroid autoimmune thyroiditis. Furthermore, connotation between environmental and endocrine risk factors in the etiology of the disease in genetically susceptible individuals were discussed. Thus, endocrinologists’ potential hurdles in cancer and thyroid nodules field include unreliable biomarkers, lack of distinct therapeutic alternatives due to genetic difference. Precision medicine data may improve their diagnostic and therapeutic capabilities using artificial intelligence.

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Section: Application Of Data Science In the Treatment Of Autoimmune Thyroid Diseasesmentioning

confidence: 99%

Section: Application Of Data Science In the Diagnosis Of Autoimmune Thyroid Diseases 41 Application Of Data Science In The Diagnosis Of Gmentioning

confidence: 99%

Section: Importance Of Data Science In Thyroid Diseasesmentioning

confidence: 99%

See 1 more Smart Citation

Application of Data Science Approaches to Investigate Autoimmune Thyroid Disease in Precision Medicine

Ajayi¹,

Adebayo²,

Adebayo³

et al. 2022

Hypothyroidism - New Aspects of an Old Disease

View full text Add to dashboard Cite

show abstract

“…Machine learning algorithms such as SVM and decision trees were also applied to predict thyroid diseases by using dataset from UCI machine learning repository [143]. For instance, Begum et al [144] proposed an advanced system based on data mining algorithms for diagnosing thyroid disorder.…”

Section: Title Accuracy Relative Demeritsmentioning

confidence: 99%

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Imran

Zaman²,

Imran

et al. 2021

Electronics

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Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as `machine learning’, blockchain, `Internet of Things or IoT’, and keywords conjoined with `healthcare’ and `health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.

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“…The experimental analysis concluded support vector machine to be the best technique for thyroid disorder diagnosis as it achieved highest accuracy. Begum and Parkav [5] evaluated KNN with naïve bayes and SVM for the prediction of thyroid disease. The authors also found out the correlation of TSH, T3, and T4 towards hyperthyroidism and hyporthyroidism.…”

Section: IImentioning

confidence: 99%

Performance Evaluation of SVM and Random Forest for the Diagnosis of Thyroid Disorder

2021

IJRASET

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Thyroid Disorder is the most common condition in the medical world, caused by the thyroid gland's failure to secrete hormones. Two separate machine learning (ML) techniques, including support vector machine (SVM) and random forest, were evaluated in this paper for thyroid disorder diagnosis. The experiment was conducted on the Thyroid Dataset, which was obtained from the UCI machine learning repository. These two machine learning techniques were compared using four performance metrics: accuracy, precision, recall, and f-score. SVM is found to be better than random forest for thyroid disorder diagnosis based on the experimental findings.

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Prediction of thyroid Disease Using Data Mining Techniques

Cited by 42 publications

References 1 publication

Application of Data Science Approaches to Investigate Autoimmune Thyroid Disease in Precision Medicine

Application of Data Science Approaches to Investigate Autoimmune Thyroid Disease in Precision Medicine

Comprehensive Survey of IoT, Machine Learning, and Blockchain for Health Care Applications: A Topical Assessment for Pandemic Preparedness, Challenges, and Solutions

Performance Evaluation of SVM and Random Forest for the Diagnosis of Thyroid Disorder

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