An Overview of Internet of Things and Machine Learning for Smart Healthcare
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Nadeem Nadeem Hashmi
Abstract
Due to COVID-19, the attempt to access medical facilities has increased. But, physical meetings are the biggest reason for the global pandemic. The healthcare industry is in a dire state of despair because of the spread of COVID, constrained health workers, and infrastructure. Healthcare accommodations are costlier than ever due to various reasons such as the global population getting busier and the rise in the number of chronic diseases. Nowadays, there is a high demand for healthcare support due to the increase in the aging population, the COVID-19 crisis, its postdisease difficulties, and other chronic diseases. Treating all the patients at hospitals is not practical nowadays due to limited healthcare facilities. The patients’ care is all dependent on visits to the hospital physically, and to some extent, calls, text messages, and emails. This way it was not possible for the doctors to monitor the health of the patients continuously and assist them whenever needed. Recently, the Internet of Things (IoT) and various machine learning approaches have been widely explored in this sector. The IoT-based healthcare methods provide quality and efficiency during treatment. The IoT along with machine learning techniques can transform the healthcare industry by making it more accessible and less costly and make healthcare more facile by equipping the users with pocket amicable medical facilities. With IoT-enabled devices, remote monitoring can enable patients to better connect with doctors, thereby minimizing physical visits. It can equip doctors to easily keep track of their patient’s health by utilizing wearables like fitness bands that can monitor various healthcare parameters. All these systems mostly use machine learning or deep learning approaches. This way, they will know whether to offer any immediate medical care or to make any changes to the current treatment. This chapter gives a detailed discussion on IoT and machine learning in the healthcare sector and the security concerns in this domain along with the major research challenges.
Abstract
Due to COVID-19, the attempt to access medical facilities has increased. But, physical meetings are the biggest reason for the global pandemic. The healthcare industry is in a dire state of despair because of the spread of COVID, constrained health workers, and infrastructure. Healthcare accommodations are costlier than ever due to various reasons such as the global population getting busier and the rise in the number of chronic diseases. Nowadays, there is a high demand for healthcare support due to the increase in the aging population, the COVID-19 crisis, its postdisease difficulties, and other chronic diseases. Treating all the patients at hospitals is not practical nowadays due to limited healthcare facilities. The patients’ care is all dependent on visits to the hospital physically, and to some extent, calls, text messages, and emails. This way it was not possible for the doctors to monitor the health of the patients continuously and assist them whenever needed. Recently, the Internet of Things (IoT) and various machine learning approaches have been widely explored in this sector. The IoT-based healthcare methods provide quality and efficiency during treatment. The IoT along with machine learning techniques can transform the healthcare industry by making it more accessible and less costly and make healthcare more facile by equipping the users with pocket amicable medical facilities. With IoT-enabled devices, remote monitoring can enable patients to better connect with doctors, thereby minimizing physical visits. It can equip doctors to easily keep track of their patient’s health by utilizing wearables like fitness bands that can monitor various healthcare parameters. All these systems mostly use machine learning or deep learning approaches. This way, they will know whether to offer any immediate medical care or to make any changes to the current treatment. This chapter gives a detailed discussion on IoT and machine learning in the healthcare sector and the security concerns in this domain along with the major research challenges.
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
