Wearable IoT intelligent recommender framework for a smarter healthcare approach

Abstract

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

Editorial: 40th anniversary: contemporary library research

“…The Internet of things (IoT) is now becoming ubiquitous (Sun et al , 2021; Chohan et al , 2021; Bokharaei Nia et al , 2021). Khan et al (2022) surveyed academic libraries in Nanjing, China.…”

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“…Hot topics include machine learning, large datasets, deep learning and high-level languages. Readers may also be interested in reading recent articles on AI library service innovation (Okunlaya et al , 2022), patterns of AI policies (Wang et al , 2023a), intelligent recommendations (Bokharaei Nia et al , 2023; Nawaz et al , 2023; Senthil Kumaran and Latha, 2023; Shi et al , 2023), library robotic process automation (Basumatary et al , 2023; Chin and Chiu, 2023; Lin et al , 2022; Wójcik, 2023), smart libraries (Khan et al , 2023; Wang, 2023b), AI awareness in libraries (Harisanty et al , 2022; Yoon et al , 2022), AI in library repositories (Nugroho et al , 2023) and ChatGPT (Afjal, 2023; Bin-Hady et al , 2023). Further, readers may be interested in our special section on smart cities (Chang and Abdel-Basset, 2022).…”

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“…Bokharaei Nia et al (2023) proposed a conceptual framework for health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms to recommend relevant wearable IoT devices. Their prototype result showed a high accuracy rate in social media sentiment analysis and their machine-learning algorithms over multiple disease-detection stages.…”