Thyroid Disease Classification Using Machine Learning Algorithms

Khalid, Salman; Sonuç, Emrullah

doi:10.1088/1742-6596/1963/1/012140

Cited by 40 publications

(8 citation statements)

References 9 publications

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“…The authors [ 15 ] evaluated the performance of the thyroid disease classification using various machine learning algorithms. SVM, RF, DT, NB, LR, K nearest neighbor (KNN), and MLP are used for disease prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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Thyroid disease prediction has emerged as an important task recently. Despite existing approaches for its diagnosis, often the target is binary classification, the used datasets are small-sized and results are not validated either. Predominantly, existing approaches focus on model optimization and the feature engineering part is less investigated. To overcome these limitations, this study presents an approach that investigates feature engineering for machine learning and deep learning models. Forward feature selection, backward feature elimination, bidirectional feature elimination, and machine learning-based feature selection using extra tree classifiers are adopted. The proposed approach can predict Hashimoto’s thyroiditis (primary hypothyroid), binding protein (increased binding protein), autoimmune thyroiditis (compensated hypothyroid), and non-thyroidal syndrome (NTIS) (concurrent non-thyroidal illness). Extensive experiments show that the extra tree classifier-based selected feature yields the best results with 0.99 accuracy and an F1 score when used with the random forest classifier. Results suggest that the machine learning models are a better choice for thyroid disease detection regarding the provided accuracy and the computational complexity. K-fold cross-validation and performance comparison with existing studies corroborate the superior performance of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

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“…Other than the previously mentioned classifier such as DT, KNN and SVM, the year 2022 saw the emergence of Random Forest as a formidable model for thyroid disease prediction. A study by Islam S. et al introduced Random Forest as a champion mode, achieving an accuracy rate of 98.93% [10]. This finding was further supported by a research conducted by Alyas T. et al, where the Random Forest model emerged as the top performer, registering a 94.8% accuracy rate, outdoing both DT and KNN [11].…”

Section: Related Workmentioning

confidence: 77%

Prediction of Thyroid Disease using Machine Learning Approaches and Featurewiz Selection

Shiuh,

Khai,

Xin

et al. 2023

JTEC

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Thyroid disease is one of the most disturbing hormonal disorders faced by the global population. To help the healthcare industry to diagnose the disorder rapidly and accurately, supervised machine learning algorithms and feature selection were introduced to play an essential role in predicting whether a patient has developed thyroid disease from his/her various characteristics. Therefore, in this work, a new feature selection library was introduced, which was the Featurewiz in the Python library. The goals were to present the performance of the Featurewiz library and to decide on a remarkable model for thyroid disease prediction among several machine learning models, such as Decision Tree, K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Support Vector Classifier, and ensembled machine learning algorithms (Random Forest and Extreme Gradient Boost). A data set consisting of records of thyroid patients in Australia was used to develop the machine-learning models. After the data set was cleaned, exploratory data analysis was carried out. The models were then built in two ways: without feature selection and with feature selection. The feature selection process was conducted by using a new Python library called Featurewiz. The performances of the models from the two operations were evaluated using three performance metrics, including accuracy, F1-score, and AUC (Area Under Curve) value from ROC (Receiver Operating Characteristics Curve). From the two operations, the results are similar in the way that tree-based models, especially those formed by the ensemble method, outperform the statistical models. Initially, in the process without feature selection, the champion model is XGBoost with 99.23% accuracy, while Random Forest ranks second with 98.79% accuracy. However, after the feature selection, the result reveals that the champion model is Random Forest. This model achieves an improvement of 0.66% in accuracy (99.45%), making it the best model from both operations. The model also scores 0.99 and 0.97 in F1-score and AUC values, respectively. The valuable insights gained from this study can serve as a comprehensive framework for machine learning applications in predicting thyroid illness. Additionally, the study highlights the advantageous utilization of the Python feature selection library, Featurewiz. With the combination of Featurewiz and machine learning applications, medical authorities can save time and reduce the risk of misdiagnosis when identifying patients with thyroid disease.

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“…Thyroid Disease Classification Using Machine Learning Algorithm [1] Khalid Salman and Emrullahsonac The first model all the characteristics consisting of 16 inputs and one output were taken, and the result of the accuracy of the random forest algorithm was 98.93, which is the highest accuracy among the other algorithms. In the second embodiment, the following characteristics were omitted based on a previous study.…”

Section: Literature Surveymentioning

confidence: 99%

Thyroid Detection using Machine learning

Adhav

Jadhao

Markad

et al. 2023

Data Analytics and Artificial Intelligence

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Thyroid is a gland which is present in front of the neck, wrapped around the windpipe (trachea). Thyroid shape is like Butterfly that’s smaller in the middle and 2 side wings which are around the throat. As Thyroid is a gland it is important in our body when thyroid produces too much thyroid hormones then that condition is called hyperthyroidism and if thyroid gland produces less thyroid hormones then that condition is called hypothyroidism. Machine learning is one of the most important tools to classify the diseases nowadays whether a person has a disease or not like Cancer detection, kidney disease detection or Diabetes etc. Our project is to predict whether a person has Thyroid disease or not. Whether he has hypothyroidism or hyperthyroidism, or he is not suffering from thyroid disease.

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Thyroid Disease Classification Using Machine Learning Algorithms

Cited by 40 publications

References 9 publications

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Prediction of Thyroid Disease using Machine Learning Approaches and Featurewiz Selection

Thyroid Detection using Machine learning

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