[Retracted] Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Alyas, Tahir; Hamid, Muhammad; Alissa, Khalid; Faiz, Tauqeer; Tabassum, Nadia; Ahmad, Aqeel

doi:10.1155/2022/9809932

Cited by 59 publications

(32 citation statements)

References 26 publications

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“…We did not deploy the feature selection approach with these previous studies; we just deployed their approach and experiments on our used dataset. We deployed study [ 19 ] which used RF for the thyroid disease prediction. Similarly, we deployed study, [ 21 ] which used DT for thyroid disease prediction.…”

Section: Resultsmentioning

confidence: 99%

“…However, the authors did not consider hyperthyroid predication. Alyas et al [ 19 ] performed a comparative analysis of the machine learning techniques DT, RF, KNN, and artificial neural network (ANN) to detect thyroid disease. The tests were conducted on the largest dataset and considered both sampled and unsampled data for thyroid disease prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Chaganti

Rustam

Díez

et al. 2022

Cancers

View full text Add to dashboard Cite

show abstract

“…al proposed their study over Cleveland dataset with the help of the BN classifier with an 85 % prediction accuracy. Various machine learning algorithm for the prediction of thyroid disease prediction was used by authors T. Alyas et al [8]. Their results show that KNN and Random Forest algorithm outperform for disease prediction.…”

Section: Related Workmentioning

confidence: 99%

“…Method Used Accuracy (%) Findings [6] BN 85 Different machine learning algorithms were applied to Cleveland data to evaluate the performance. [7] DT, RF, KNN, ANN 94.85 A random forest algorithm was used for oversampled and unsampled data for thyroid prediction [8] GA-CFS 99.2 A hybrid classifier using GA-CFS was proposed for heart disease detection. [9] BN 77…”

Section: Referencesmentioning

confidence: 99%

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Kushwaha

Thirunavukkarasan

2022

Preprint

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Heart disease is the leading cause of death around the globe, killing more people than cancer. A study published in the Journal of the American Heart Association shows that gene variants in our genes can increase our risk of heart failure by as much as 40%. To make matters worse, the number of people with advanced heart failure is expected to rise by 30% by the year 2030. Some of the most common heart disease symptoms include chest pain, shortness of breath, and fatigue. It has been observed that machine learning can provide efficient heart disease prediction over a large amount of data thereby, empowering the patients and health care experts with the knowledge to make more accurate decisions at an appropriate time-bound. The central aspect of our proposed research is to build up a hybrid machine learning classifier using a Genetic Algorithm with a Super learner ensemble (GA-SLE) for the detection of heart disease with increased accuracy. In our study, the proposed system is compared with various machine learning algorithms such as Random Forest (RF), Multilayer Perceptron (MLP), K- Nearest Neighbour (KNN), Extra Tree (ET), Extreme Gradient Boosting (XGB), Super Vector Classifier (SVC), Stochastic Gradient Descent(SGD), AdaBoost, Decision Tree(CART), Gradient Boosting(GBM) using 10-Fold cross-validation and hyperparameter tuning. A regressed comparative analysis of all three approaches has been performed with the help of a table and plot. The proposed GA-SLE outperforms all other ML classifiers, obtaining a prediction accuracy of 99.8% with minimum error loss. As per our findings, the current GA-SLE classifier enables the practitioner to detect and diagnose the sickness in the early stage of the disease.

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“…Tahir Alyas et al, [11] have introduced an empirical technique for thyroid infection prediction using the AI approach. In this article, various AI computations such as ANN, KNN, random forest algorithm, and decision tree on the dataset have experimented to predict the disease more easily in light of the boundaries de ned in the dataset.…”

Section: Related Workmentioning

confidence: 99%

Efficient Feature Extraction Based on Optimized Gated Recurrent Unit Recurrent Neural Network for Automatic Thyroid Prediction

Saka

Krishna

2022

Preprint

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In this paper, develop Efficient Feature Extraction Based Recurrent Neural Network (EFERNN). Initially, the databases are gathered from the open-source system. After that, the pre-processing technique is developed for correcting missing values by the normalization technique of min-max normalization. The pre-processed data is utilized for feature extraction by using feature extraction techniques such as Two-Level Feature Extraction (TLFE) techniques. In level1, the ranked filter feature set technique is utilized to rank the features based on doctor recommendations. In order to execute the label-driven validation, ranking measures are used. In level 2, features are ranked and selected using a variety of metrics, including info gain, gain ratio, chi-square, and relief. In level 2, the effective features are chosen from the feature set using a fuzzy-based composite measure. In order to categorise thyroid disease from the databases, the Optimized Gated Recurrent Unit - Recurrent Neural Network (GRU-RNN) is used. In the GRU-RNN, the weight is selected with the assistance of the COOT Optimization Algorithm. The suggested method is put into practise in MATLAB, and its effectiveness is assessed by taking into account statistical measurements like kappa, accuracy, precision, recall, sensitivity, specificity and F Measure. To validate the proposed technique, it is compared with conventional techniques such as Deep Belief Neural Network (DBN). Artificial Neural Network (ANN) and Support Vector Machine (SVM).

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[Retracted] Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Cited by 59 publications

References 26 publications

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

GA-SLE: A hybrid algorithm for heart disease prediction using feature selection mechanism

Efficient Feature Extraction Based on Optimized Gated Recurrent Unit Recurrent Neural Network for Automatic Thyroid Prediction

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