为大数据医疗保健系统优化机器学习机制,预测疾病风险因素
Venkata Nagaraju Thatha1, Silpa Chalichalamala2, Udayaraju Pamula3
1Department of Information Technology, MLR Institute of Technology, Hyderabad, India.
Scientific reports
|April 24, 2025
概括
一个新的Deep Red Fox信念预测系统 (DRFBPS) 能够有效地识别心脏病风险因素. 这种人工智能工具增强了医疗分析中的早期诊断和预防性护理.
科学领域:
- 心血管健康 心血管健康
- 人工智能在医学中的应用
- 预测分析是一种预测分析.
背景情况:
- 由于现代生活方式的因素,如压力和不良饮食,心脏病的患病率正在增加.
- 早期识别心脏病风险因素对于及时干预和改善患者结果至关重要.
- 现有的预测模型往往会遇到诸如低于最佳特征选择和过拟合等问题.
研究的目的:
- 引入和实施一个新的深红狐信仰预测系统 (DRFBPS),用于预测心脏病风险.
- 解决传统预测方法在特征选择和模型准确性方面的局限性.
- 评估DRFBPS在早期诊断和预防性护理的医疗分析中的有效性.
主要方法:
- 数据收集和预处理以确保数据质量.
- 使用红狐优化算法进行特征选择.
- 使用开发的DRFBPS模型预测心脏病风险因素.
- 使用准确度,F分数,精度,AUC,回忆和错误率等指标验证DRFBPS性能.
主要成果:
- 该DRFBPS模型证明了对心脏病风险因素的准确和可靠预测.
- 性能验证证实了该模型在多个评估指标上的有效性.
- 该研究强调DRFBPS是医疗分析的实用工具.
结论:
- DRFBPS为心脏病风险评估中的预测建模提供了一个强大的框架.
- 该系统的应用范围扩展到临床决策和远程患者监测.
- 在心血管健康方面,DRFBPS显示了提高早期诊断和预防策略的巨大潜力.
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