一个复杂的屏幕智能框架用于预测糖尿病在远程医疗保健
Koteeswaran Seerangan1, Premalatha Gunasekaran2, Nithya Rekha Sivakumar3
1Department of Computer Science and Engineering, R.M.K. Engineering College (Autonomous), Chennai 601206, Tamil Nadu, India.
Diagnostics (Basel, Switzerland)
|February 27, 2026
概括
这项研究介绍了BOLD模型,这是一种用于早期糖尿病预测的AI工具. 黄铜优化基于学习的糖尿病预测 (BOLD) 模型在诊断糖尿病方面实现了高准确性和效率,改善了患者的治疗结果.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于疾病预测和预测
- 医学诊断中的深度学习
背景情况:
- 糖尿病是一种普遍的代谢性疾病,其特点是长期高血糖.
- 早期发现糖尿病对于降低疾病严重程度和相关风险至关重要.
- 现有的糖尿病预测人工智能模型往往缺乏稳定性和精度.
研究的目的:
- 设计和开发一种自动化工具,用于使用一种新的AI方法来诊断慢性疾病.
- 为了提高基于AI的糖尿病预测的稳定性,可靠性和精度.
- 为远程医疗应用引入布拉斯优化基于学习的糖尿病预测 (BOLD) 模型.
主要方法:
- BOLD模型利用优化集成的深度学习来提高性能.
- 数据预处理包括糖尿病数据集的分割,规范化和清理.
- 功能选择是使用Brassy Pelican优化 (BPO) 进行的.
- 分类是使用狩猎优化循环神经网络长期短期记忆 (RNN-LSTM) 进行的,用于超参数调整的鹿狩猎优化 (DHO).
主要成果:
- 大胆框架实现了高性能指标:RMSE为0.015,科恩的卡帕为0.99,精度为0.991,召回为0.99,准确度为0.996,AUC为0.99.
- 该模型在多个数据集中证明了有效性,包括PIDD,印度尼西亚糖尿病数据库和病数据集.
- 验证证实了该框架能够准确预测糖尿病的能力.
结论:
- BOLD模型为糖尿病预测提供了一个高度准确和高效的解决方案.
- 该框架在0.8秒的非常短的处理时间内实现了卓越的性能.
- 这种速度和精度的结合使得BOLD模型成为远程医疗保健和早期疾病干预的宝贵工具.
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