Glucose Data Classification for Diabetic Patient Monitoring

[EN] Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P.Rghioui, A.; Lloret, J.; Parra-Boronat, L.; Sendra, S.; Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences. 9(20):1-15. https://doi.org/10.3390/app9204459S115920Rghioui, A., Sendra, S., Lloret, J., & Oumnad, A. (2016). Internet of Things for Measuring Human Activities in Ambient Assisted Living and e-Health. Network Protocols and Algorithms, 8(3), 15. doi:10.5296/npa.v8i3.10146Zhang, Y., Gravina, R., Lu, H., Villari, M., & Fortino, G. (2018). PEA: Parallel electrocardiogram-based authentication for smart healthcare systems. Journal of Network and Computer Applications, 117, 10-16. doi:10.1016/j.jnca.2018.05.007Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2018). Mining productive-periodic frequent patterns in tele-health systems. Journal of Network and Computer Applications, 115, 33-47. doi:10.1016/j.jnca.2018.04.014Aboufadel, E., Castellano, R., & Olson, D. (2011). Quantification of the Variability of Continuous Glucose Monitoring Data. Algorithms, 4(1), 16-27. doi:10.3390/a4010016Katon, W. J., Rutter, C., Simon, G., Lin, E. H. B., Ludman, E., Ciechanowski, P., … Von Korff, M. (2005). The Association of Comorbid Depression With Mortality in Patients With Type 2 Diabetes. Diabetes Care, 28(11), 2668-2672. doi:10.2337/diacare.28.11.2668Riazul Islam, S. M., Daehan Kwak, Humaun Kabir, M., Hossain, M., & Kyung-Sup Kwak. (2015). The Internet of Things for Health Care: A Comprehensive Survey. IEEE Access, 3, 678-708. doi:10.1109/access.2015.2437951Lloret, J., Canovas, A., Sendra, S., & Parra, L. (2015). A smart communication architecture for ambient assisted living. IEEE Communications Magazine, 53(1), 26-33. doi:10.1109/mcom.2015.7010512Xiao, Z., Tan, X., Chen, X., Chen, S., Zhang, Z., Zhang, H., … Min, H. (2015). An Implantable RFID Sensor Tag toward Continuous Glucose Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2015.2415836Wang, H.-C., & Lee, A.-R. (2015). Recent developments in blood glucose sensors. Journal of Food and Drug Analysis, 23(2), 191-200. doi:10.1016/j.jfda.2014.12.001Ahmed, H. B., & Serener, A. (2016). Effects of External Factors in CGM Sensor Glucose Concentration Prediction. Procedia Computer Science, 102, 623-629. doi:10.1016/j.procs.2016.09.452Siddiqui, S. A., Zhang, Y., Lloret, J., Song, H., & Obradovic, Z. (2018). Pain-Free Blood Glucose Monitoring Using Wearable Sensors: Recent Advancements and Future Prospects. IEEE Reviews in Biomedical Engineering, 11, 21-35. doi:10.1109/rbme.2018.2822301Fortino, G., Parisi, D., Pirrone, V., & Di Fatta, G. (2014). BodyCloud: A SaaS approach for community Body Sensor Networks. Future Generation Computer Systems, 35, 62-79. doi:10.1016/j.future.2013.12.015Kanchan, B. D., & Kishor, M. M. (2016). Study of machine learning algorithms for special disease prediction using principal of component analysis. 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC). doi:10.1109/icgtspicc.2016.7955260Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10-18. doi:10.1145/1656274.1656278Huda, S., Yearwood, J., Jelinek, H. F., Hassan, M. M., Fortino, G., & Buckland, M. (2016). A Hybrid Feature Selection With Ensemble Classification for Imbalanced Healthcare Data: A Case Study for Brain Tumor Diagnosis. IEEE Access, 4, 9145-9154. doi:10.1109/access.2016.264723

An evaluation of the utilization of remote sensing in resource and environmental management of the Chesapeake Bay region

A nine-month study was conducted to assess the effectiveness of the NASA Wallops Chesapeake Bay Ecological Program in remote sensing. The study consisted of a follow-up investigation and information analysis of actual cases in which remote sensing was utilized by management and research personnel in the Chesapeake Bay region. The study concludes that the NASA Wallops Chesapeake Bay Ecological Program is effective, both in terms of costs and performance

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Internet of Things for Sustainable Human Health

The sustainable health IoT has the strong potential to bring tremendous improvements in human health and well-being through sensing, and monitoring of health impacts across the whole spectrum of climate change. The sustainable health IoT enables development of a systems approach in the area of human health and ecosystem. It allows integration of broader health sub-areas in a bigger archetype for improving sustainability in health in the realm of social, economic, and environmental sectors. This integration provides a powerful health IoT framework for sustainable health and community goals in the wake of changing climate. In this chapter, a detailed description of climate-related health impacts on human health is provided. The sensing, communications, and monitoring technologies are discussed. The impact of key environmental and human health factors on the development of new IoT technologies also analyzed

Special Libraries, February 1951

Volume 42, Issue 2https://scholarworks.sjsu.edu/sla_sl_1951/1001/thumbnail.jp

Preventive audiology

This book is an original scholarly book that introduces the concept of preventive audiology, with a specific focus on the African context, which is in line with the South African re-engineered primary healthcare strategy as well as the World Health Organisation’s approach. The book reflects on contextually relevant and responsive evidence-based perspectives, grounded in an African context on preventive audiology, in four major ear and hearing burdens of disease within the South African context: (1) early hearing detection and intervention, (2) middle ear pathologies, (3) ototoxicity, and (4) noise-induced hearing loss. The book represents innovative research, seen from both a South African and global perspective. It offers new discourse and argues for a paradigm shift in how audiology is theorised and performed, particularly in low-and-middle-income country contexts. The goal of this book is to motivate a paradigm shift in how the ear and hearing care is approached within this low-and-middle-income country context while arguing for Afrocentric best practice evidence that leads to next practice