Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models
Abstract Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weig...
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| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02780-0 |
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| author | Ali Raza Fatma Eid Elisabeth Caro Montero Irene Delgado Noya Imran Ashraf |
| author_facet | Ali Raza Fatma Eid Elisabeth Caro Montero Irene Delgado Noya Imran Ashraf |
| author_sort | Ali Raza |
| collection | DOAJ |
| description | Abstract Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations. Prompt diagnosis and appropriate treatment are crucial in managing thyroid disorders and improving patients’ quality of life. Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. An open-access thyroid disease dataset based on 3,772 male and female patient observations is used for this research experiment. This study uses the nominal continuous synthetic minority oversampling technique (SMOTE-NC) for data balancing and a fine-tuned light gradient booster machine (LGBM) technique to diagnose thyroid illness and handle class imbalance problems. The proposed SNL (SMOTE-NC-LGBM) approach outperformed the state-of-the-art approach with high-accuracy performance scores of 0.96. We have also applied advanced machine learning and deep learning methods for comparison to evaluate performance. Hyperparameter optimizations are also conducted to enhance thyroid diagnosis performance. In addition, we have applied the explainable Artificial Intelligence (XAI) mechanism based on Shapley Additive exPlanations (SHAP) to enhance the transparency and interpretability of the proposed method by analyzing the decision-making processes. The proposed research revolutionizes the diagnosis of thyroid disorders efficiently and helps specialties overcome thyroid disorders early. |
| format | Article |
| id | doaj-art-c012b30a738f45bb95cde40be4f5552f |
| institution | OA Journals |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-c012b30a738f45bb95cde40be4f5552f2025-08-20T02:08:16ZengBMCBMC Medical Informatics and Decision Making1472-69472024-11-0124111810.1186/s12911-024-02780-0Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning modelsAli Raza0Fatma Eid1Elisabeth Caro Montero2Irene Delgado Noya3Imran Ashraf4Department of Software Engineering, University of LahoreDepartment of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan UniversityUniversidad Europea del AtlanticoUniversidad Europea del AtlanticoDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations. Prompt diagnosis and appropriate treatment are crucial in managing thyroid disorders and improving patients’ quality of life. Thyroid illness affects millions worldwide and can significantly impact their quality of life if left untreated. This research aims to propose an effective artificial intelligence-based approach for the early diagnosis of thyroid illness. An open-access thyroid disease dataset based on 3,772 male and female patient observations is used for this research experiment. This study uses the nominal continuous synthetic minority oversampling technique (SMOTE-NC) for data balancing and a fine-tuned light gradient booster machine (LGBM) technique to diagnose thyroid illness and handle class imbalance problems. The proposed SNL (SMOTE-NC-LGBM) approach outperformed the state-of-the-art approach with high-accuracy performance scores of 0.96. We have also applied advanced machine learning and deep learning methods for comparison to evaluate performance. Hyperparameter optimizations are also conducted to enhance thyroid diagnosis performance. In addition, we have applied the explainable Artificial Intelligence (XAI) mechanism based on Shapley Additive exPlanations (SHAP) to enhance the transparency and interpretability of the proposed method by analyzing the decision-making processes. The proposed research revolutionizes the diagnosis of thyroid disorders efficiently and helps specialties overcome thyroid disorders early.https://doi.org/10.1186/s12911-024-02780-0Machine learningDeep learningThyroid disordersMedical diagnosisExplainable artificial intelligence |
| spellingShingle | Ali Raza Fatma Eid Elisabeth Caro Montero Irene Delgado Noya Imran Ashraf Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models BMC Medical Informatics and Decision Making Machine learning Deep learning Thyroid disorders Medical diagnosis Explainable artificial intelligence |
| title | Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| title_full | Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| title_fullStr | Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| title_full_unstemmed | Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| title_short | Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| title_sort | enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models |
| topic | Machine learning Deep learning Thyroid disorders Medical diagnosis Explainable artificial intelligence |
| url | https://doi.org/10.1186/s12911-024-02780-0 |
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