The COVID-19 pandemic and other ongoing health crises have underscored the need for prompt healthcare services worldwide. The traditional healthcare system, centered around hospitals and clinics, has proven inadequate in the face of such challenges. Intelligent wearable devices, a key part of conventional healthcare, leverage Internet of Things (IoT) technology to collect extensive data related to the environment, as well as psychological, behavioral, and physical health. Managing the substantial data generated by these wearables and other IoT devices in healthcare poses a significant challenge, potentially impeding decision-making processes. Recent interest has grown in applying data analytics for extracting information, gaining insights, and making predictions. Additionally, machine learning (ML), known for addressing various networking challenges, has seen increased implementation to enhance IoT systems in healthcare. This chapter focuses exclusively on exploring the hurdles encountered when integrating ML methods into the IoT healthcare sector. We offer a comprehensive summary of current research challenges and potential opportunities, categorized into three scenarios: IoT-based, ML-based, and the implementation of ML methodologies in the healthcare industry via the IoT. We highlight the difficulties faced by existing methodologies, providing valuable insights for future researchers, healthcare professionals, and government agencies. This ensures they stay updated on the latest developments in big data analytics for intelligent healthcare utilizing ML.

翻译：COVID-19疫情及其他持续的全球健康危机凸显了对即时医疗服务的迫切需求。以医院和诊所为核心的传统医疗体系在应对此类挑战时已显不足。作为传统医疗关键组成部分的智能可穿戴设备，借助物联网技术采集与环境、心理、行为及身体健康相关的海量数据。管理这些可穿戴设备及其他医疗物联网设备产生的庞大数据量构成重大挑战，可能阻碍决策过程。近年来，运用数据分析提取信息、获取洞察及进行预测的兴趣日益增长。此外，以解决多种网络挑战而闻名的机器学习技术，在增强医疗物联网系统方面的应用也日益增加。本章专攻探索将机器学习方法整合至物联网医疗领域时遇到的障碍。我们全面总结当前的研究挑战与潜在机遇，并将其分为三类场景：基于物联网、基于机器学习，以及通过物联网在医疗行业中实施机器学习方法论。我们强调现有方法论面临的困难，为未来研究人员、医疗专业人士及政府机构提供宝贵见解，确保其及时掌握利用机器学习实现智能医疗的大数据分析最新进展。