Department of Software Engineering, National University of Modern Languages, Islamabad 44000, Pakistan.
Department of Artifical Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
Comput Intell Neurosci. 2023 Mar 1;2023:1102715. doi: 10.1155/2023/1102715. eCollection 2023.
Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.
传染病对人类生命的生存总是令人警惕的，是公共卫生领域关注的重点。因此，早期诊断这些传染病是现代医疗保健系统的高要求。新型一般传染病，如冠状病毒，是 2020 年在全球范围内导致数百万人死亡的传染病。因此，早期、稳健地识别一般传染病是现代智能医疗保健系统的理想要求。本系统研究是根据 Kitchenham 指南设计的，并为一般传染病的稳健识别设定了不同的 RQ（研究问题）。从 2018 年到 2021 年，使用了四个电子数据库，IEEE、ACM、Springer 和 ScienceDirect，用于提取研究工作。这些提取的研究通过不同的机器学习技术（包括深度学习和联邦学习模型）提供了不同的方案，以准确识别一般传染病。还引入了一个框架来共享使用机器学习模型检测传染病的过程。经过过滤过程，提取了 21 项研究，并将其映射到定义的 RQ 上。在未来，通过可穿戴健康监测笼可以实现传染病的早期诊断。此外，这些笼子将有助于通过在疾病早期阶段检测严重疾病来减少时间和死亡率。
