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Internet of Things with Machine Learning-Based Smart Cardiovascular Disease Classifier for Healthcare in Secure Platform
Published in Ankan Bhattacharya, Bappadittya Roy, Samarendra Nath Sur, Saurav Mallik, Subhasis Dasgupta, Internet of Things and Data Mining for Modern Engineering and Healthcare Applications, 2023
Sima Das, Jaya Das, Subrata Modak, Kaushik Mazumdar
H. Kupwade Patil and R. Seshadri [37] reported that with the rising expense of healthcare and rising health insurance rates, preventive healthcare and wellness are more important than ever. Furthermore, the healthcare business has undergone a paradigm shift as a result of the current wave of medical record digitization. As a result, the healthcare business is dealing with an increase in data volume, complexity, diversity, and timeliness. Big data emerges as a viable solution with the potential to alter the healthcare industry as healthcare experts hunt for every conceivable way to reduce costs while enhancing the care process, delivery, and management. This shift in focus from reactive to proactive healthcare may result in a reduction in overall health care costs.
Revolutionizing Healthcare
Published in Bharat Bhushan, Nitin Rakesh, Yousef Farhaoui, Parma Nand Astya, Bhuvan Unhelkar, Blockchain Technology in Healthcare Applications, 2022
Kavitha Rajamohan, Sangeetha Rangasamy, Surekha Nayak, R. Anuradha, Aarthy Chellasamy
Preventive healthcare facilitates healthier practices like nutritional diet and mental and physical fitness leading to a better lifestyle for wellbeing. The advent of wearable devices and implantable devices has a great role in preventive healthcare. As per the study conducted by WHO (2003), there were 75% adult patients who were not adhering to the prescriptions and not following the doctor’s suggestions properly in terms of medicines, diet and exercise. Blockchain combined with IoT will not only help patients to be on track but also the healthy patients to track their performance.
IoT-Based WBAN Health Monitoring System with Security
Published in S. Velliangiri, Sathish A. P. Kumar, P. Karthikeyan, Internet of Things, 2020
IoT-based WBAN system changed the way people think about the healthcare management system just like people communicate with each other using internet and search for information independent of place and distance. This motivates for proactive and preventive healthcare with the improvement of quality of life and low-cost healthcare.
Big data analytics in healthcare: a systematic literature review
Published in Enterprise Information Systems, 2020
Sayantan Khanra, Amandeep Dhir, A. K. M. Najmul Islam, Matti Mäntymäki
To determine the appropriate keywords, a search was performed on Google Scholar with the phrase ‘big data analytics in healthcare.’ The most commonly related terms were identified from the first 100 search results (Khanra, Dhir, and Mäntymäki 2020). We identified from the co-occurrence of keywords that the term ‘predictive analytics’ was frequently used to refer to ‘big data analytics,’ following an approach adapted by Khanra, Dhir, and Mäntymäki (2020). Raghupathi and Raghupathi (2014) highlighted applications of big data analytics in healthcare, including analysis of patient profiles with predictive modelling to identify suitable treatments, prediction of outcomes of different treatments, and percipience of the most clinically and cost-effective treatments for the patient. Similarly, we identified that the term ‘health management’ frequently represents different components of ‘healthcare’ in the extant literature. Among the major components of health management are the clinical diagnosis, clinical research, prediction of disease transmission, preventive healthcare, health insurance, and healthcare service delivery (Kamble et al. 2019). Therefore, four search syntaxes (see Table 1) were used to represent the phrase ‘big data analytics in healthcare.’
Rethinking key issues for understanding the new challenges of disruption and digital transformation in companies and economies
Published in Behaviour & Information Technology, 2019
Patricia Ordóñez de Pablos, José Emilio Labra Gayo
The ninth paper, titled ‘Integrating TTF and IDT to Evaluate User Intention of Big Data Analytics in Mobile Cloud Healthcare System’ (by Shu Lin Wang and Shin I Lin) use big data analysis and a mobile cloud healthcare system to ‘aids young users in preventive healthcare against diabetes. It also integrates the Task-Technology Fit (TTF) and Innovation Diffusion Theory (IDT) models to evaluate user intentions to use the system, and tests this model using data collected from 374 young people. Results show that task-technology fit is significantly affected by task characteristics and technology characteristics, and also user intention of using the Big Data-based Mobile Cloud Healthcare system is affected by task-technology fit, perceived ease of use, and relative advantage. However, observability has no significant effect on user intentions of using the mobile cloud healthcare system. These findings provide some theoretical insights into the usage of the mobile cloud healthcare system’.
Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system
Published in Behaviour & Information Technology, 2019
Because mobile cloud healthcare systems integrate Big Data analysis, a Personal Health Record (PHR) can be created to enable individualised healthcare (Sa et al. 2016; Rämgård, Forsgren, and Avery 2017). This study uses big data with the association rule mining approach to implement its prototype mobile cloud healthcare system. This system can perform data mining, analyse disease symptoms, and provide health education and information regarding diabetes for preventive healthcare.