Data Science Algorithms and Techniques for Smart Healthcare Using IoT and Big Data Analytics

Syed, Liyakathunisa; Jabeen, Sadia; Manimala, S.; Elsayed, Hoda Ahmed Galal

doi:10.1007/978-3-030-03131-2_11

Cited by 44 publications

(16 citation statements)

References 22 publications

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“…In the literature, we can find a lot of healthcare techniques ranging from diagnosis to treatment and prevention [11]- [13]. The authors of [14], [15] present an overview on data analytics algorithms proposed in the literature for healthcare-based IoT applications.…”

Section: Related Workmentioning

confidence: 99%

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

et al. 2021

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Nowadays, keeping a strong and good health is one of the main concern of the general public or governments. The Internet of Things (IoT) has been emerged as an efficient solution to build smart healthcare systems deployed either at hospitals or in-home. Such networks rely on biomedical sensors which are used in electronics-based medical equipment to remotely collect vital signs of patients (pressure, temperature, hart rate, oxygen saturation etc.). Generally, these biosensors are implemented on or inside the patient's body and take three types of record data such as numerical, images and videos. However, the big data collected by various biomedical sensors along with the need of emergency detection, the limited sensor energies, and the prediction of the progress of patient situation are the major challenges for heath-based IoT applications. In order to overcome these challenges, we propose, in this paper, an efficient sensor-based data analytics for real-time patient monitoring and assessment to help both hospital and medical staff. The proposed mechanism consists in three phases: Emergency detection, adapting sensing frequency and real time prediction of patient situation. Through simulations on real health data, we show the effectiveness of our mechanism compared to other exiting techniques.

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Section: Related Workmentioning

confidence: 99%

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

et al. 2021

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“…Such systems have contributed in a number of ways to the medicinal communities from enhancing the quality of treatments given to the patients to enhancing the rapid methods of clinical decision making. There are studies conducted for remotely monitoring chronic diseases by collecting and analyzing the physiological data of the patients through the sensors connected to the patients [12]. The advent of big data has also opened many research avenues to the existing data analytics researches to explore the availability of platforms, technologies and open challenges pertinent to huge availability of healthcare data.…”

Section: Related Workmentioning

confidence: 99%

Well-Calibrated Probabilistic Machine Learning Classifiers for Multivariate Healthcare Data

Pasha

Parthiban²

2021

ijarcs

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“…Information hacking is one of the leading privacy breaches. Security risks are increasing due to lack of understanding in using technology by the health care community [114]. Big data in healthcare raises huge concerns related to security and Privacy issues of patients.…”

Section: Data Privacymentioning

confidence: 99%

Development of Novel Big Data Analytics Framework for Smart Clothing

2020

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Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested. INDEX TERMSWearable technology, smart clothing, big data, data analytics, artificial intelligence (AI), machine learning (ML) and decision-making. He is currently a Postdoctoral Researcher with the Department of Engineering, Manchester Metropolitan University. He has participated in over three projects that delivered cost effective solutions for industry. His main research interests include developing hardware and software platforms, and algorithms for data analysis and cyber security purposes.ALHUSSEIN ALBARBAR is currently a Reader with the Department of Engineering, Manchester Metropolitan University. He has well over 27 years of industrial working experience and as an Academic Active Researcher. He led and participated in over $7M of major projects and supervised over 21 research degrees including 15 doctoral studies. He has published three books, five-book chapters, over 100 technical papers in refereed journals and international conference proceedings. His current research interests include Industry 4.0 applications, renewable power systems, smart sensing, intelligent control, and monitoring algorithms used for electromechanical power plants.

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Data Science Algorithms and Techniques for Smart Healthcare Using IoT and Big Data Analytics

Cited by 44 publications

References 22 publications

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

Well-Calibrated Probabilistic Machine Learning Classifiers for Multivariate Healthcare Data

Development of Novel Big Data Analytics Framework for Smart Clothing

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