Hammoud Bassel, Semaan Aline, Benova Lenka, Elhajj Imad H
Biomedical Engineering Program, Faculty of Medicine-Maroun Semaan Faculty of Engineering and Architecture, American University of Beirut, Beirut, Lebanon.
Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium.
BMC Bioinformatics. 2025 Jul 16;26(1):180. doi: 10.1186/s12859-025-06228-8.
The practice of medicine has evolved significantly during the past decade, with the emergence of Machine Learning (ML) that offers the opportunity of personalized patient-tailored care. However, ML models still face some challenges when classifying patients where clear-cut boundaries between classes are hard to identify. In this work, we propose an ML architecture to improve the sensitivity of detecting patients in intermediate "hard-to-classify" classes.
The proposed architecture replaces a single classifier with a group of cascaded increasingly specialized classifiers: the 'Human-like', the 'Segregating', and the 'Deep' classifiers. Its effectiveness is tested, using 8 ML algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, XGBoost, CatBoost, and Artificial Neural Network) to predict the feeling of protection among healthcare workers during the COVID-19 pandemic, based on a global online survey, then validated on two other outputs.
The results show, for most algorithms, an enhanced detection of data points belonging to intermediate classes (up to 14% absolute increase in accuracy), as well as an overall improvement in the models' accuracies (up to 5.8% absolute increase). The validation experiments yielded similar results with improved accuracies for most algorithms when compared to the single classifier architecture.
This novel architecture is proving to be a very promising tool for improving accuracy of the models when classifying patients in intermediate classes, regardless of the algorithm used. Accuracy-improvement for likert-type scale measures offers an opportunity for rapidly identifying "risk-profiles" during emergencies and beyond. This applies equally to patients and healthcare providers, with potential for improving quality of care and strengthening patient-centered healthcare systems that prioritize healthcare providers' wellbeing.
在过去十年中，医学实践发生了显著变化，机器学习（ML）的出现为个性化的患者定制护理提供了机会。然而，在对类别之间界限难以明确识别的患者进行分类时，ML模型仍然面临一些挑战。在这项工作中，我们提出了一种ML架构，以提高检测处于中间“难以分类”类别的患者的敏感性。
所提出的架构用一组级联的、越来越专业化的分类器取代了单个分类器：“类人”分类器、“分离”分类器和“深度”分类器。使用8种ML算法（逻辑回归、支持向量机、K近邻、决策树、随机森林、XGBoost、CatBoost和人工神经网络）对其有效性进行测试，以根据一项全球在线调查预测COVID-19大流行期间医护人员的防护感受，然后在另外两个输出上进行验证。
结果表明,对于大多数算法，属于中间类别的数据点的检测得到了增强（准确率绝对提高高达14%），并且模型的准确率总体上有所提高（绝对提高高达5.8%）。与单个分类器架构相比，验证实验产生了类似的结果，大多数算法的准确率有所提高。
无论使用何种算法，这种新颖的架构在对处于中间类别的患者进行分类时，都被证明是提高模型准确率的非常有前途的工具。李克特量表测量的准确率提高为在紧急情况及以后快速识别“风险概况”提供了机会。这同样适用于患者和医护人员，有可能提高护理质量并加强以患者为中心的医疗系统，该系统将医护人员的福祉放在首位。
