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在老年人中优化跌倒风险诊断,使用贝叶斯分类器和模拟化
Enrique Hernandez-Laredo1, Ángel Gabriel Estévez-Pedraza1, Laura Mercedes Santiago-Fuentes2
1Tianguistenco Professional Academic Unit, Autonomous University of the State of Mexico, Tianguistenco 52640, Mexico.
Bioengineering (Basel, Switzerland)
|September 27, 2024
概括
这项研究使用模拟回火 (SA) 算法来选择落风险评估的特征. 该SA算法发现了较少的特征,改善了分类器可解释性和降低了可变性,以便更好地诊断落风险.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 跌倒风险评估对于预防老年人受伤至关重要.
- 当前的诊断工具可能很难在临床环境中应用.
- 提高跌倒风险分类器的准确性和可解释性是一个持续的挑战.
研究的目的:
- 提高跌倒风险分类器的诊断能力.
- 应用贝叶斯方法结合模拟化 (SA) 算法来进行特征选择.
- 优化功能集,以提高分类器性能和可解释性.
主要方法:
- 从181个记录中分析了47个特征,包括压力中心 (CoP) 指数和患者变量.
- 采用包装方法来使用SA算法进行特征选择.
- 优化了基于曲线下的面积 (AUC) 差异的成本函数,并使用了分层的60-20-20%数据分割.
主要成果:
- 最高的性能 (0.815±0.110) 是通过31个特征实现的.
- 一个15个特征的缩小集产生了可比性能 (0.780 ± 0.055) 与显著较低的变化和更高的可解释性.
- 在选择最佳特征子集时,SA算法被证明有效.
结论:
- 模拟回火算法是用于在跌倒风险诊断中选择特征的宝贵工具.
- 这种方法为现有的临床工具提供了替代或补充方法.
- 优化特征选择可以导致更可靠和可解释的跌倒风险评估.
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