基于机器学习的桥本氏甲状腺炎发病风险预测

Luis Jesuino de Oliveira Andrade, Gabriela Correia Matos de Oliveira, Luisa Correia Matos de Oliveira, Alcina Maria Vinhaes Bittencourt, Luis Matos de Oliveira
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摘要

简介桥本氏甲状腺炎(HT)是一种影响甲状腺功能的常见自身免疫性疾病。早期发现可及时干预,改善患者预后。传统的诊断方法依赖于临床表现和抗体检测,缺乏强有力的风险预测工具。目标:开发一种高精度的机器诊断方法:开发一种高精度的机器学习(ML)模型,用于预测甲状腺肿大的发病风险。方法:从 PubMed 获取患者数据。通过数据预处理、特征选择和探索各种 ML 模型,构建二元分类器。采用超参数优化和性能评估指标(AUC-ROC、AUC-PR、灵敏度、特异性、精确度、F1 分数)。结果:在总共 9,173 人中,有 400 人甲状腺功能正常,436 人被诊断为甲亢。患者平均年龄为 45 岁,90% 为女性。表现最好的模型的 AUC-ROC 为 0.87,AUC-PR 为 0.85,表明预测准确性很高。此外,灵敏度、特异性、精确度和 F1 分数分别达到了 85%、90%、80% 和 83%,表明该模型在识别高危人群方面非常有效。超参数调整采用随机搜索法进行了优化:本研究证明了利用 ML 准确预测高血压风险的可行性。所取得的高性能指标凸显了这一方法成为早期识别易患甲状腺炎患者并对其进行风险分层的宝贵临床工具的潜力:桥本氏甲状腺炎 机器学习 风险预测 算法
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Machine Learning-Based Prediction of Hashimoto Thyroiditis Development Risk
Introduction: Hashimoto Thyroiditis (HT) is a prevalent autoimmune disorder impacting thyroid function. Early detection allows for timely intervention and improved patient outcomes. Traditional diagnostic methods rely on clinical presentation and antibody testing, lacking a robust risk prediction tool. Objective: To develop a high-precision machine learning (ML) model for predicting the risk of HT development. Method: Data patients were acquired from PubMed. A binary classifier was constructed through data pre-processing, feature selection, and exploration of various ML models. Hyperparameter optimization and performance evaluation metrics (AUC-ROC, AUC-PR, sensitivity, specificity, precision, F1 score) were employed. Results: Out of a total of 9,173 individuals, 400 subjects within this cohort exhibited normal thyroid function, while 436 individuals were diagnosed with HT. The mean patient age was 45 years, and 90% were female. The best performing model achieved an AUC-ROC of 0.87 and AUC-PR of 0.85, indicating high predictive accuracy. Additionally, sensitivity, specificity, precision, and F1 score reached 85%, 90%, 80%, and 83% respectively, demonstrating the model's effectiveness in identifying individuals at risk of HT development. Hyperparameter tuning was optimized using a Random Search approach. Conclusion: This study demonstrates the feasibility of utilizing ML for accurate prediction of HT risk. The high performance metrics achieved highlight the potential for this approach to become a valuable clinical tool for early identification and risk stratification of patients susceptible to HT. Keywords: Hashimoto Thyroiditis, Machine Learning, Risk Prediction, Algorithms.
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