Diaproteo:基于蛋白质组概况的糖尿病早期检测的监督学习框架
Hamza Shahab Awan1,2, Fahad Alturise3, Tamim Alkhalifah4
1Department of Computer Science, Comsats University Islamabad, Lahore, Pakistan.
Digital health
|August 4, 2025
概括
机器学习和深度学习可以准确地预测糖尿病 (DM). 额外树木算法实现了91.52%的准确性,显示了早期诊断和个性化医疗保健的潜力.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 糖尿病是一种广泛的慢性代谢疾病,对健康和经济产生重大影响.
- 早期检测和干预对于缓解DM并发症和改善患者预后至关重要.
- 预测建模为主动的DM管理提供了一个有希望的途径.
研究的目的:
- 调查监督机器学习和深度学习算法的有效性,以预测糖尿病.
- 用不同的数据集评估和比较各种预测模型的性能.
- 为准确的DM预测确定最佳算法.
主要方法:
- 利用监督学习算法,包括经典的机器学习和深度学习架构.
- 使用的数据集包括临床特征,遗传标记和生活方式变量.
- 使用准确性,精度,回忆和F1分数等指标评估模型性能,包括独立测试和10倍交叉验证.
主要成果:
- 额外树木算法在独立测试集中获得了最高准确率 (91.52%) 的F1得分为0.91.
- 卷积神经网络 (CNN) 在十倍交叉验证过程中表现出强的性能,准确率为87.03%,F1得分为84.82%.
- 对比分析表明,额外树木算法的性能优于其他评估模型.
结论:
- 机器学习和深度学习模型可以准确预测糖尿病.
- 额外树木算法显示了可靠的DM预测的巨大潜力.
- 这些发现支持开发早期干预和个性化糖尿病护理工具.
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