通过移动应用程序利用混合卷积封闭的复发性糖尿病预测和严重程度分级模型
Alhuseen Omar Alsayed1, Nor Azman Ismail1, Layla Hasan1
1Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahur, Johor, Malaysia.
PeerJ. Computer science
|March 10, 2025
概括
一个新的深度学习模型,卷积门循环单元 (CGRU),准确地预测糖尿病及其严重程度. 这种先进的方法克服了现有模型的局限性,以改善早期检测和医疗保健结果.
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:
- 糖尿病有显著的发病率和死亡率,需要早期检测以预防并发症.
- 现有的用于糖尿病预测的机器学习模型在准确性,可靠性和数据不平衡方面面临挑战.
- 可靠的糖尿病预测模型对于及时干预和改善患者结果至关重要.
研究的目的:
- 引入一种新的深度学习机制 - - 卷积门循环单元 (CGRU),用于准确检测和评估糖尿病的严重程度.
- 解决目前机器学习模型在预测糖尿病方面的局限性.
- 增强从数据中提取时间和空间特征,以提高预测准确度.
主要方法:
- 开发了一个深度学习框架,利用卷积门循环单元 (CGRU) 进行糖尿病预测.
- 数据预处理涉及对BRFSS数据集的归算,特征删除和规范化.
- 采用聚类算法,根据患者的特征和识别的模式来对糖尿病严重程度进行分类.
主要成果:
- 拟议的CGRU模型在糖尿病预测方面实现了99.9%的高准确率.
- 与基于注意力的CNN和Ensemble ML模型等最先进的方法相比,CGRU框架表现出卓越的性能.
- 聚类算法证明在识别微妙模式方面具有有益作用,以实现更准确和可靠的严重程度分类.
结论:
- CGRU模型显著提高了早期糖尿病检测和诊断,从而带来更好的医疗保健结果.
- 拟议的深度学习方法为糖尿病预测和严重程度评估提供了更准确和可靠的方法.
- 未来的研究应该集中在验证CGRU模型在不同的数据集上,以进一步确定其通用性.
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