Comparison of classification model results.

This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several algorithms are utilized such as k-nearest neighbor, Random Forest (RF), C4.5, Artificial Neural Network, and Support Vector Machine (SVM). Their performance is assessed using anonymous patients’ data. Then, a proposed double classifier based on Henry Gas Solubility Optimization (HGSO) is developed with Aquila optimizer (AQO). It is implemented for feature selection to classify patients’ outcome status into four states. Those are mortality, morbidity, improved, or the same. The double classifiers are evaluated via various performance metrics including recall, precision, F-measure, accuracy, and sensitivity. Another contribution of this research is the original use of hybrid technique based on RF-SVM and HGSO to predict patient outcome status with high accuracy. It determines outcome status relationship with age and mode of trauma. The algorithm is tested on more than 1000 anonymous patients’ data taken from a Neurosurgical unit of Mansoura International Hospital, Egypt. Experimental results show that the proposed method has the highest accuracy of 99.2% (with population size = 30) compared with other classifiers.本研究旨在预测神经外科儿童、成人及老年颅脑创伤患者的预后结局。鉴于机器学习（Machine Learning, ML）算法在医疗领域具有重要应用价值，本研究开展了多种机器学习技术的对比实验研究。本研究选用了k近邻、随机森林（Random Forest, RF）、C4.5、人工神经网络以及支持向量机（Support Vector Machine, SVM）等多种算法，并基于匿名患者数据对各算法的性能进行了评估。在此基础上，本研究提出了一种融合亨利气体溶解度优化算法（Henry Gas Solubility Optimization, HGSO）与鹫珠鸡优化器（Aquila Optimizer, AQO）的双分类器模型，通过特征选择将患者预后状态划分为死亡、伤残、病情改善及病情无变化四类，并采用召回率、精确率、F1值、准确率及灵敏度等多项性能指标对该双分类器进行性能评估。本研究的另一项创新贡献在于，首次将基于随机森林-支持向量机与亨利气体溶解度优化算法的混合技术应用于患者预后预测，实现了高精度的预后状态判定，并分析了患者预后状态与年龄及创伤模式的关联关系。本研究所用数据集取自埃及曼苏拉国际医院神经外科科室的1000余例匿名患者数据。实验结果表明，所提方法在种群规模为30时可达到99.2%的最高准确率，优于其他对比分类器。