Department of Software Engineering, University of Lahore, Lahore, 54000, Pakistan.
Department of Applied Artificial Intelligence, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, Republic of Korea.
BMC Med Inform Decis Mak. 2024 Nov 29;24(1):364. doi: 10.1186/s12911-024-02780-0.
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.
甲状腺疾病包括一系列影响甲状腺的疾病，导致甲状腺功能亢进或甲状腺功能减退，这会显著影响新陈代谢和整体健康。甲状腺功能减退会导致身体机能减缓，导致疲劳、体重增加、抑郁和对寒冷敏感等症状。甲状腺功能亢进会导致新陈代谢增加，导致快速体重减轻、焦虑、易怒和心悸等症状。及时诊断和适当治疗对于管理甲状腺疾病和提高患者生活质量至关重要。甲状腺疾病影响着全球数百万人，如果不治疗，会显著影响他们的生活质量。本研究旨在提出一种基于人工智能的甲状腺疾病早期诊断的有效方法。本研究实验使用了一个基于 3772 名男性和女性患者观察结果的开放获取甲状腺疾病数据集。本研究使用标称连续合成少数群体过采样技术（SMOTE-NC）进行数据平衡，并使用微调的轻梯度提升机（LGBM）技术诊断甲状腺疾病并处理类别不平衡问题。所提出的 SNL（SMOTE-NC-LGBM）方法的性能优于最先进的方法，具有 0.96 的高精度性能得分。我们还应用了先进的机器学习和深度学习方法进行比较评估。超参数优化也被用来提高甲状腺诊断性能。此外，我们还应用了基于 Shapley Additive exPlanations (SHAP) 的可解释人工智能（XAI）机制，通过分析决策过程来增强所提出方法的透明度和可解释性。本研究通过高效地诊断甲状腺疾病，帮助医学专家早期发现甲状腺疾病，从而彻底改变了甲状腺疾病的诊断方式。
