Interactive Thyroid Disease Prediction System Using Machine Learning Technique

Tyagi, Amit Kumar; Mehra, Ritika; Saxena, Aditya

doi:10.1109/pdgc.2018.8745910

Cited by 87 publications

(30 citation statements)

References 18 publications

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“…The logistic regression algorithm's accuracy and sensitivity results are being selected as the best algorithm among the others. In [74] and [76], KNN and SVM algorithms were applied to two different datasets and features. The result in [74] showed that the SVM algorithm is better than KNN On the other hand, in [76] KNN algorithm override SVM by about 3%.…”

Section: Discussionmentioning

confidence: 99%

“…In [74] and [76], KNN and SVM algorithms were applied to two different datasets and features. The result in [74] showed that the SVM algorithm is better than KNN On the other hand, in [76] KNN algorithm override SVM by about 3%. In research [76], the decision tree's accuracy is better than the random forest, around 1%.…”

Section: Discussionmentioning

confidence: 99%

“…In 2018, Ankita Tyagi and et al [74] suggested a different machine learning methods for diagnosing and preventing thyroid disease. ML has an essential role in the disease detection process.…”

Section: H Deep Learningmentioning

confidence: 99%

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

176

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

176

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“…By using data mining techniques different kinds of works have been done for thyroid disorders. Ankita Tyagi et al(2018) used Machine learning algorithm, Support Vector Machine(SVM), KNN and Decision Tree with the dataset containing minimum number of parameters taken from UCI machine learning repository for the prediction of estimated risk of a person's chance to get thyroid disease [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Computational way of Classifying Thyroid Disorder Patients and Identifying the Similar Patients using a Novel Method

Jamuna*,

Kumar

2020

IJITEE

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The thyroid hormones secreted by thyroid gland are interrelated with many metabolic processes of our body. Any dysfunction of thyroid gland leads to thyroid diseases. Hypothyroidism and hyperthyroidism are the very common thyroid disorders which affect the large number of people nowadays. Prediction of thyroid diseases at right time and giving suitable medicines to the patients help them to overcome the health problems. A machine learning technique will definitely assist the physicians for the prediction and treatment of thyroid diseases. In this work, the datasets are taken from UCI repository and Fuzzy- C Means algorithm is used for the clustering the thyroid diseases

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“…Siddhi Vinod Parodkar, Amita Dessai [4] proposed neuro-fuzzy classifier to diagnose thyroid diseases, and said that other major diseases can be predicted by training the classifier with appropriate dataset and improve the performance of medical diagnosis system. Ankita Tyagi, Ritika Mehra, Ritika Mehra [5] proposed various techniques in machine learning. Also, proposed diagnosis for the prevention of thyroid.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Classifiers with Machine Learning based Thyroid Disease Prediction System

Chanukotimath*,

B S

2020

IJRTE

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Most of the people in different nations are suffering from Thyroid related diseases and these are lifelong. Many people are unaware of having Thyroid related diseases. Main cause for this is due to improper functioning of Thyroid gland secreting Thyroid hormone which regulates body metabolism. In this paper we have made survey on classifiers like Decision Tree C4.5(J48), Multilayer Perceptron, Naïve Bayes by measuring TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area, PRC Area and developed a prediction system for Thyroid diseases. For training and testing the classifiers we have used Thyroid dataset from UCI repository. Dataset consists of 9172 records containing 29 attribute values and 1 diagnosis class value. The diagnosis class value consists of different types Thyroid disease conditions like hyperthyroid conditions, hypothyroid conditions, binding protein, general health, replacement therapy, antithyroid treatment and miscellaneous. The proposed prediction system model capable of predicting type of Thyroid disease whether a person is suffering or not.

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Interactive Thyroid Disease Prediction System Using Machine Learning Technique

Cited by 87 publications

References 18 publications

The Role of Machine Learning Algorithms for Diagnosing Diseases

The Role of Machine Learning Algorithms for Diagnosing Diseases

Computational way of Classifying Thyroid Disorder Patients and Identifying the Similar Patients using a Novel Method

Assessment of Classifiers with Machine Learning based Thyroid Disease Prediction System

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