Suresh Shruthi, Newton David T, Everett Thomas H, Lin Guang, Duerstock Bradley S
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.
Department of Statistics, Purdue University, West Lafayette, IN, United States.
Front Neuroinform. 2022 Aug 10;16:901428. doi: 10.3389/fninf.2022.901428. eCollection 2022.
Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.
特征选择在机器学习算法的发展中起着至关重要的作用。了解特征对模型的影响及其生理相关性可以提高模型性能。这在医疗保健领域尤其有用，因为在该领域中需要用相对少量的数据来识别疾病状态。自主神经反射异常（AD）就是这样一个例子，在这种神经疾病的管理不善可能会给脊髓损伤患者带来严重后果。我们探索了不同的特征选择方法，以提高机器学习模型在检测AD发作方面的性能。我们展示了使用从心电图、皮肤神经活动、血压和温度中提取的36个特征的数据集所使用的不同技术以及理想的指标。表现最佳的算法是具有五个相关特征的五层神经网络，其在检测AD方面的准确率达到了93.4%。本文中的技术可以应用于无数医疗保健数据集，从而深入探索并改进机器学习模型的开发。通过关键的特征选择，可以使用较小的数据集设计出更好的机器学习算法来检测特定的疾病状态。
