The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.

诸如COVID-19之类的慢性疾病的爆发再次呼吁为全球公民提供紧急医疗保健设施。最近的大流行暴露了传统医疗体系的不足，即仅医院和诊所无法应对这种情况。智能和互联可穿戴设备是帮助当今医疗保健解决方案的主要技术之一。物联网（IoT）的进步使这些可穿戴设备能够以前所未有的规模收集数据。这些可穿戴设备收集与我们的身体，行为和心理健康相关的面向上下文的信息。物联网的可穿戴设备和其他医疗保健设备生成的大数据是一项具有挑战性的管理任务，可能会对决策中心的推理过程产生负面影响。将大数据分析应用于挖掘信息，提取知识以及进行预测/推理最近引起了极大的关注。机器学习是已成功应用于解决各种网络问题（例如路由，流量工程，资源分配和安全性）的另一个研究领域。最近，我们看到了基于ML的技术的应用激增，以改善各种IoT应用程序。尽管对大数据分析和机器学习进行了广泛的研究，但仍缺乏专门针对物联网医疗保健行业中基于ML的大数据分析技术发展的研究。在本文中，我们对机器学习技术在医疗保健领域大数据分析中的应用进行了全面的综述。此外，强调了现有技术的优点和缺点以及各种研究挑战。我们的研究将为医疗保健从业者和政府机构提供见解，以使他们能够很好地掌握基于ML的智能医疗保健大数据分析的最新趋势。