Barua Shaibal, Begum Shahina, Ahmed Mobyen Uddin
School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
Stud Health Technol Inform. 2015;211:241-8.
Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.
机器学习算法在计算机科学研究中发挥着重要作用。临床科学中传感器数据收集的最新进展导致了用于患者诊断和预后的复杂、异构数据处理与分析。基于对这些传感器数据的人工分析来诊断和治疗患者既困难又耗时。因此，开发基于知识的系统以支持临床医生进行决策很重要。然而，有必要开展实验工作来比较不同机器学习方法的性能，以帮助为数据集的特定特征选择合适的方法。本文比较了三种流行的机器学习方法，即基于案例的推理、神经网络和支持向量机，利用手指温度和心率变异性来诊断车辆驾驶员的压力。实验结果表明，基于案例的推理在分类准确率方面优于其他两种方法。基于案例的推理使用手指温度和心率变异性对压力进行分类时分别达到了80%和86%的准确率。相反，神经网络和支持向量机使用这两种生理信号时准确率均低于80%。
