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使用深度学习和进化计算的综合方法,提高对维生素D缺乏水平的预测
Ahmed Alzahrani1, Muhammad Zubair Asghar2
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
PeerJ. Computer science
|March 10, 2025
概括
这项研究引入了一种新的非侵入性方法,用于通过深度学习和进化计算来检测维生素D缺乏 (VDD) 的严重程度. 混合模型实现了高精度,在预测医疗诊断方面提供了有前途的进步.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 维生素D缺乏 (VDD) 是一个具有严重后果的全球健康问题.
- 目前对VDD的诊断方法通常是侵入性的,并且不利用广泛的医疗保健数据.
- 越来越需要非侵入性VDD预测技术.
研究的目的:
- 开发一种新的,非侵入性的方法来检测维生素D缺乏 (VDD) 水平.
- 将深度学习与进化计算 (EC) 结合起来,以提高VDD预测.
- 为了提高VDD诊断的准确性和效率.
主要方法:
- 使用混合深度学习模型集成卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 网络.
- 纳入进化计算 (EC) 方法,特别是遗传算法 (GA),用于特征选择和超参数优化.
- 在用于VDD预测的基准数据集上对拟议的模型进行了评估.
主要成果:
- 拟议的混合深度学习和EC模型实现了高性能指标.
- 在基准数据集上实现了97%的准确性,96%的精度,97%的回忆率和96%的F1得分.
- 与现有的VDD检测方法相比,其表现优越.
结论:
- 开发的方法提供了一种有效的,非侵入性的方法来预测维生素D缺乏 (VDD) 的严重程度.
- 将深度学习与进化计算结合在一起,为应对复杂的医疗保健挑战提供了一个强大的战略.
- 这项研究推进了预测性医疗保健模型,并突出了AI在诊断方面的潜力.
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