Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing
Abstract Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-025-00225-9 |
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| author | Md. Rezaul Islam Aniruddha Islam Chowdhury Sharmin Shama Md. Masudul Hasan Lamyea |
| author_facet | Md. Rezaul Islam Aniruddha Islam Chowdhury Sharmin Shama Md. Masudul Hasan Lamyea |
| author_sort | Md. Rezaul Islam |
| collection | DOAJ |
| description | Abstract Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses these challenges by employing a standard machine learning model, enhanced with comprehensive clinical feature analysis and an ensemble learning technique. By leveraging machine learning, we can identify key risk factors and improve diagnostic accuracy. To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. Class balancing techniques, particularly random oversampling, significantly improved classification performance. Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges. |
| format | Article |
| id | doaj-art-9d2fb4465ab54014b49c025073bae6ca |
| institution | Kabale University |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-9d2fb4465ab54014b49c025073bae6ca2025-02-02T12:33:46ZengSpringerDiscover Artificial Intelligence2731-08092025-01-015112910.1007/s44163-025-00225-9Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancingMd. Rezaul Islam0Aniruddha Islam Chowdhury1Sharmin Shama2Md. Masudul Hasan Lamyea3Department of Computer Science and Engineering, Shahjalal University of Science and TechnologyDepartment of Computer Science and Engineering, Dhaka International UniversityDepartment of Computer Science and Engineering, Dhaka International UniversityDepartment of Computer Science and Engineering, Dhaka International UniversityAbstract Thyroid disorders are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate prediction of disease progression and understanding the interplay of clinical features are essential for effective diagnosis and treatment. Our study addresses these challenges by employing a standard machine learning model, enhanced with comprehensive clinical feature analysis and an ensemble learning technique. By leveraging machine learning, we can identify key risk factors and improve diagnostic accuracy. To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. Class balancing techniques, particularly random oversampling, significantly improved classification performance. Our experimental results demonstrate that the proposed model outperforms existing methods, achieving 100% sensitivity and 99.72% accuracy using the XGBoost algorithm and SelectKBest feature selection. By addressing feature reduction and high class-imbalance, the ensemble ML classifier with hard voting proves more effective in handling classification challenges.https://doi.org/10.1007/s44163-025-00225-9Thyroid Disease PredictionMachine Learning AlgorithmsData VisualizationClass balancing techniquesXGBoost AlgorithmConfusion Matrices |
| spellingShingle | Md. Rezaul Islam Aniruddha Islam Chowdhury Sharmin Shama Md. Masudul Hasan Lamyea Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing Discover Artificial Intelligence Thyroid Disease Prediction Machine Learning Algorithms Data Visualization Class balancing techniques XGBoost Algorithm Confusion Matrices |
| title | Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing |
| title_full | Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing |
| title_fullStr | Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing |
| title_full_unstemmed | Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing |
| title_short | Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing |
| title_sort | enhanced thyroid disease prediction using ensemble machine learning a high accuracy approach with feature selection and class balancing |
| topic | Thyroid Disease Prediction Machine Learning Algorithms Data Visualization Class balancing techniques XGBoost Algorithm Confusion Matrices |
| url | https://doi.org/10.1007/s44163-025-00225-9 |
| work_keys_str_mv | AT mdrezaulislam enhancedthyroiddiseasepredictionusingensemblemachinelearningahighaccuracyapproachwithfeatureselectionandclassbalancing AT aniruddhaislamchowdhury enhancedthyroiddiseasepredictionusingensemblemachinelearningahighaccuracyapproachwithfeatureselectionandclassbalancing AT sharminshama enhancedthyroiddiseasepredictionusingensemblemachinelearningahighaccuracyapproachwithfeatureselectionandclassbalancing AT mdmasudulhasanlamyea enhancedthyroiddiseasepredictionusingensemblemachinelearningahighaccuracyapproachwithfeatureselectionandclassbalancing |