Machine Learning Techniques for Thyroid Disease Diagnosis - A Review

Razia, Shaik; Rao, M. R. Narasinga

doi:10.17485/ijst/2016/v9i28/93705

Cited by 41 publications

(13 citation statements)

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“…The training was achieved by a back propagation method after setting network properties and raining parameters. Back propagation method has been used on some research as Aktan et al (2016); [22,[42][43][44][45]. The network was tested with thyroid hormones data collected from cases and controls and measured drinking water fluoride concentration.…”

Section: Application Of Ann S and Anfis Modelmentioning

confidence: 99%

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Kheradpisheh

Mahvi

Mirzaei

et al. 2018

J Environ Health Sci Engineer

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Background Artificial neural networks (ANNs) and adaptive neural-fuzzy Inference system (ANFIS) are the best solutions to finding the correlation between some water parameters and human hormones. The correlation between thyroid stimulating hormone (TSH) and drinking water fluoride studied by ANN S and ANFIS models in Yazd city. Method In this study, eighty people with thyroid gland disorder and 213 healthy people invited. Their thyroid hormones and fluoride drinking water analyzed. Results The result of ANFIS showed R 2 = 0.81 for test and R 2 = 0.85 for train in all cases and controls data. This results were R 2 = 0.73 and R 2 = 0.81 for ANNs respectively. Conclusion This models can be used as an alternative for show correlation between Drinking Water Fluoride and TSH Hormone and R 2 = 0.85 gained from ANFIS was the best.

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Section: Application Of Ann S and Anfis Modelmentioning

confidence: 99%

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Kheradpisheh

Mahvi

Mirzaei

et al. 2018

J Environ Health Sci Engineer

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“…15 In most of these studies, it has been attempted to increase the accuracy of diagnosis by applying different classification methods. [16][17][18][19][20][21][22][23][24][25] In the present study, a real dataset was used to predict thyroid disorder. One of the main drawbacks of most of the presented approaches is that they have not been evaluated on real datasets.…”

Section: Tablementioning

confidence: 99%

“…Many studies have only used laboratory variables for the diagnosis of thyroid disorder. [16][17][18][19][20][21][22][23][24][25] As far as we know, in limited studies, both sets of laboratory tests and some of the symptoms variables were used in the classification model. 30 Various research works in the field of thyroid classification based on different data mining techniques are mentioned.…”

Section: Tablementioning

confidence: 99%

Diagnosis of hypothyroidism using a fuzzy rule-based expert system

Sajadi

Borzouei

Mahjub

et al. 2019

Clinical Epidemiology and Global Health

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Background: Early and accurate diagnosis of many diseases is critical to their treatment. Today, the classification models based on fuzzy intelligent systems help the uncertainty conditions in medicine, as well as the classification of diseases. The main goal of this study is to diagnose the most common thyroid disorders, hypothyroidism, using a fuzzy rule-based expert system. Methods: In this study, the data from patients who referred to Imam Khomeini Clinic and Shahid Beheshti Hospital in Hamadan west of Iran were collected. The data contain 305 subjects in three classes which are normal, subclinical hypothyroidism and hypothyroidism. Collected variables include demographic, symptoms as well as laboratory tests. In order to the diagnosis of Thyroid disorders, a fuzzy rule-based classifier was designed. Predictive performance of this model was compared with a multinomial logistic regression model in terms of the accuracy and the area under a ROC curve (AUC) as well as sensitivity and specificity. Results: The results showed that designed fuzzy rule-based system works well in thyroid disorder prediction with about 97% accuracy. Also, the fuzzy classifier has a better performance than the logistic regression model, especially for the subclinical hypothyroidism class. Conclusions: Fuzzy rule-based classifier by utilizing overlapping sets has improved the efficiency of classification and decision-making systems. Also, by providing the possibility of using linguistic variables in the decisionmaking process, easier interpretation, can be used by doctors who are not familiar with modeling concepts.

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“…The outcome of the thyroid disease is growing drastically and provides a new path for the biological methods and to analyze the existence of thyroid disease. The various different neural network modeling are designed for the prediction of thyroid disease by using parameter estimation methods [1].…”

Section: A Literature Reviewmentioning

confidence: 99%

Constituent Depletion and Divination of Hypothyroid Prevalance using Machine Learning Classification

Devi*¹,

Shil²,

Katyayan³

et al. 2019

IJITEE

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With the vast growth of technology, the world is moving towards different style of instant food habits which lead to the irregular functioning of the body organs. One such victim problem we face is the existence of hypothyroid in the body. Hypothyroid is the under active thyroid circumstance, where the thyroid gland does not produce required amount of essential hormones. The prediction of hypothyroid still remains as a challenging task due to the non availability of exact symptoms. By keeping this analysis in mind, this paper focus on prediction of hypothyroid based on the clinical parameters. The hypothyroid dataset from the UCI machine learning repository is used for predicting the existence of hypothyroid using machine learning classification algorithms. The prediction of existence of hypothyroid is carried out in four ways. Firstly, the raw data set is fitted with various classification algorithms to find the existence of hypothyroid. Secondly, the data set is tailored by the Ada Boost Regressor algorithm to extract the important features from the hypothyroid dataset. Then the extracted feature importance of the hypothyroid dataset is then fitted to the various classification algorithms. Thirdly, the hypothyroid dataset is subjected to the dimensionality reduction using principal component analysis. The PCA reduced hypothyroid dataset is then fitted with classification algorithms to predict the existence of hypothyroid. Fourth, the performance analysis is done for the raw data set, Feature importance AdaBoost hypothyroid dataset and PCA reduced hypothyroid dataset by comparing the performance metrics like precision, recall, FScore and Accuracy. This paper is implemented by python scripts in Anaconda Spyder Navigator. Experimental Result shows that the Random Forest, Naive Bayes and Logistic regression have the accuracy of 99.5 for the raw dataset, feature importance reduced dataset and the accuracy of 99.8 for the five component reduced PCA dataset.

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Machine Learning Techniques for Thyroid Disease Diagnosis - A Review

Cited by 41 publications

References 0 publications

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Diagnosis of hypothyroidism using a fuzzy rule-based expert system

Constituent Depletion and Divination of Hypothyroid Prevalance using Machine Learning Classification

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