深:一种有效的深度学习框架,用于慢性病预测
Dina Saif1, Amany M Sarhan1, Nada M Elshennawy1
1Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt.
Health information science and systems
|December 4, 2023
概括
这项研究介绍了用于早期发现慢性病 (CKD) 的三种AI模型. 一个深层组合模型在预测CKD方面取得了卓越的准确性,提前6-12个月.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 是一个重要的全球健康问题.
- 慢性病的晚期表现往往导致严重的损伤,强调了早期检测的必要性.
- 目前的诊断方法可能无法在足够早的时间发现CKD,以防止疾病的晚期进展.
研究的目的:
- 开发和评估用于早期检测慢性病 (CKD) 的先进预测模型.
- 在临床表现前6至12个月预测CKD的潜在发生.
- 为了比较深度学习模型的有效性,包括CNN,LSTM和新的深度合奏方法,用于CKD预测.
主要方法:
- 开发了三种预测模型:卷积神经网络 (CNN),长期短期记忆 (LSTM) 和深层合奏模型.
- 深集团模型集成了CNN,LSTM和LSTM-BLSTM分类器,使用多数投票技术.
- 性能评估是使用两个不同的公共数据集进行的,以评估预测的准确性.
主要成果:
- 与单个CNN和LSTM模型相比,Deep Ensemble模型表现出卓越的性能.
- 深层组合模型实现了高预测准确性:0.993对于6个月的预测和0.992对于12个月的预测.
- 这些结果表明,它具有很强的早期和准确的CKD风险识别能力.
结论:
- 拟议的深层组合模型为慢性病的早期预测提供了一个有前途的工具.
- 利用深度学习,特别是组合方法,可以显著提高CKD检测的及时性和准确性.
- 通过先进的人工智能模型进行早期预测,有可能改善患者的治疗结果,并减少晚期病的负担.
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