通过实施多层前神经网络来评估顺序回归模型的性能:高血压病例研究
Mohamad N Adnan1, Wan Muhamad Amir W Ahmad1, Hazik B Shahzad1,2
1School of Dental Sciences, Universiti Sains Malaysia, Kota Bharu, MYS.
Cureus
|March 20, 2024
概括
结合统计和神经网络技术的新混合模型有效地识别高血压和失脂症风险因素. 这种方法显示出对患者健康结果的卓越预测能力.
科学领域:
- 心血管研究研究心血管研究
- 计算统计学 计算统计学
- 医疗保健中的机器学习
背景情况:
- 高血压 (高血压) 是一个普遍的全球健康问题,显著增加心血管疾病 (CVD),中风和脏疾病的风险.
- 高血压和失脂症的同时发生在患者管理和风险预测方面带来了复杂的挑战.
研究的目的:
- 开发和验证一个新的混合模型,集成引导,有序后勤回归和多层前神经网络 (MLFFNN).
- 确定和分析与高血压相关的关键因素,在患有失脂血症的患者中.
主要方法:
- 使用RStudio软件使用先进的计算统计建模技术.
- 开发并验证了一种混合模型,该模型结合了引导,有序后勤回归和MLFFNN.
- 分析了马来西亚大学科学家医院33名参与者的数据,分为培训和测试数据集.
主要成果:
- 混合模型表现出良好的模型匹配,由预测平均平方误差 (MSE),平均绝对偏差 (MAD) 和精度所证明.
- 该模型表现出卓越的预测能力,在预测MSE方面表现优于传统的回归模型.
- 发现的关键预测因素包括婚姻状况,吸烟状况,缩血压,禁食血糖和高密度脂蛋白水平.
结论:
- 开发的混合模型为分析高血压和失脂症并发症提供了强大而优异的方法.
- 该研究强调了整合各种计算统计方法的有效性,以提高临床环境中的预测准确性.
- 这些发现强调了确定风险因素在治疗高血压和脂质不良症患者中的重要性.
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