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在马来西亚,机器学习模型用于预测2型糖尿病并发症
Mohamad Zulfikrie Abas1, Kezhi Li2, Wan Yuen Choo1
1Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Asia-Pacific journal of public health
|April 19, 2025
概括
机器学习模型可以准确地预测马来西亚2型糖尿病患者的糖尿病并发症. 光梯度增强机 (LGBM) 在识别死亡率和视网膜病变等风险方面表现最好.
科学领域:
- 医疗信息学医学信息学
- 公共卫生 公共卫生
- 机器学习在医疗保健中的应用
背景情况:
- 马来西亚的2型糖尿病 (T2D) 造成了严重的健康负担.
- 预测糖尿病并发症对于有效的患者管理和干预至关重要.
- 现有的预测模型可能无法完全捕捉T2D进展的复杂性.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测马来西亚T2D患者的五个关键糖尿病并发症.
- 为此预测任务确定最有效的ML算法.
- 支持将ML工具纳入糖尿病护理的临床实践.
主要方法:
- 利用了马来西亚国家糖尿病登记和死亡登记 (2011-2021) 的数据.
- 开发了所有原因死亡率,视网膜病变,病,缺血性心脏病 (IHD) 和脑血管疾病 (CeVD) 的预测模型.
- 测试了7个ML算法,包括Light Gradient Boosting Machine (LGBM),并使用ROC-AUC分数比较了它们的性能.
主要成果:
- 光梯度增强机 (LGBM) 算法在测试模型中表现出优异的性能.
- 伦理同性恋者取得了显著的ROC-AUC得分:0.84 (所有原因死亡率),0.71 (视网膜病变),0.71 (脏病),0.66 (IHD) 和0.74 (CeVD).
- 研究队列包括90,933名2型糖尿病患者.
结论:
- 机器学习,特别是LGBM,显示出在现实世界马来西亚T2D人口中预测糖尿病并发症的巨大潜力.
- 这些预测模型可以帮助制定有针对性的预防策略和个性化糖尿病管理.
- 为了更广泛的临床应用和多样化的群体,建议进行进一步的验证和优化.
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