ArticleBioMed research international2022
Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 90 citations in OpenAlex.
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- ELTIRADS framework for thyroid nodule classification integrating elastography, TIRADS, and radiomics with interpretable machine learning.Scientific reports · 2025Article
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- Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.BMC medical informatics and decision making · 2024Article
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- Automatic Classification of Nodules from 2D Ultrasound Images Using Deep Learning Networks.Journal of imaging · 2024Article
- Identification of important symptoms and diagnostic hypothyroidism patients using machine learning algorithms.Annals of medicine and surgery (2012) · 2024Article
- A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.Briefings in bioinformatics · 2024Article
- Analysis and interpretability of machine learning models to classify thyroid disease.PloS one · 2024Article
- Retracted: Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.BioMed research international · 2024Article
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- Article
- An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.Sensors (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
- Retraction · 2024-03-20Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists' mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity.
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Registered trials
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