Proposed Conceptual IoT-Based Patient Monitoring Sensor for Predicting and Controlling Dengue
DOI:
https://doi.org/10.18486/ijcsnt/12.1.157Keywords:
Dengue Prediction, Internet of things, Patient Monitoring Sensor, Medical InformaticsAbstract
Dengue is an epidemic-disease by mosquito-borne virus that spreads easily in geographically affected areas. Dengue outbreak management system has increasingly being developed in identifying and controlling the spread of dengue but with some limitation. The growing development of wearable Internet of Things (IoT), cloud computing, analytical approaches provide better alternative for dengue prediction and control. Previous literature collecting various parameters for analyzing dengue pattern. However, with the increasing number of analytical solution there is a need to investigate the high related parameters that should be use in analyzing and monitoring dengue outbreak before designing an IoT devices. Besides, there is a need for an alternative to ensure that early warning can be detected by monitoring the patient infected by the dengue, this paper aims to propose a conceptual IoT-based patient monitoring sensor for predicting and controlling dengue outbreak. Therefore, this paper provides a recent review of the latest methods and algorithms used to design wearable sensor for patient monitoring in dengue outbreak. Based on the review, this paper outlines the parameters that will be used in dengue for analyzing purposes. Finally, a conceptual IoT-based patient monitoring sensor were proposed comprising three different sensors to further work with analytical tools for dengue prediction pattern. The proposed conceptual design may help researcher to use the parameter identified for development of IoT sensor for dengue outbreak.
References
V. Racloz, R. Ramsey, S. Tong, and W. Hu, “Surveillance of dengue fever virus: A review of epidemiological models and early warning systems,” PLoS Neglected Tropical Diseases, Vol. 6, No. 5, 2012. DOI: https://doi.org/10.1371/journal.pntd.0001648
T. J. Sheehan and L. M. DeChello, “A space-time analysis of the proportion of late stage breast cancer in Massachusetts, 1988 to 1997,” International Journal of Health Geographics, Vol. 4, p. 15, 2005. DOI: https://doi.org/10.1186/1476-072X-4-15
A. B. Chavanpatil and P. S. S. Sonawane, “To Predict Heart Disease Risk and Medications Using Data Mining Techniques With an IoT Based Monitoring System for Post Operative Heart Disease Patients,” Proceedings of the Journal on Emerging Trends in Technology (IJETT), Vol. 4, pp. 8274–8281, 2017.
M. Bhavani and S. V. Kumar, “A Data Mining Approach for Precise Diagnosis of Dengue Fever,” International Journal of Latest Trends in Engineering and Technology, Vol. 7, No. 4, pp. 352–359, 2016. DOI: https://doi.org/10.21172/1.74.048
D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, and G. Escobar, “Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients,” Health Affairs, Vol. 33, No. 7, 2014. DOI: https://doi.org/10.1377/hlthaff.2014.0041
M. Palaniyandi, “The Environmental Aspects of Dengue and Chikungunya Outbreaks in India: GIS for Epidemic Control,” International Journal of Mosquito Research, Vol. 35, No. 12, pp. 35–40, 2014.
A. Raji, P. Golda Jeyasheeli, and T. Jenitha, “IoT Based Classification of Vital Signs Data for Chronic Disease Monitoring,” Proceedings of the 10th International Conference on Intelligent Systems and Control, 2016. DOI: https://doi.org/10.1109/ISCO.2016.7727048
R. Kumar and M. P. Rajasekaran, “An IoT Based Patient Monitoring System Using Raspberry Pi,” in 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE’16), pp. 1–4, 2016. DOI: https://doi.org/10.1109/ICCTIDE.2016.7725378
V. Shnayder, B. Chen, K. Lorincz, T. R. F. Fulford-Jones, and M. Welsh, “Sensor Networks for Medical Care,” 2005. DOI: https://doi.org/10.1145/1098918.1098979
Y. Zhang, M. Qiu, C. W. Tsai, M. M. Hassan, and A. Alamri, “Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data,” IEEE Systems Journal, Vol. 99, 2015.
E. L. Pang and H. S. Loh, “Current Perspectives on Dengue Episode in Malaysia,” Asian Pacific Journal of Tropical Medicine, Vol. 9, No. 4, pp. 395–401, 2016. DOI: https://doi.org/10.1016/j.apjtm.2016.03.004
C. Y. Ling, O. Gruebner, A. Krämer, and T. Lakes, “Spatio-Temporal Patterns of Dengue in Malaysia: Combining Address and Sub-District Level,” Geospatial Health, Vol. 9, No. 1, pp. 131–140, 2014. DOI: https://doi.org/10.4081/gh.2014.11
Y. L. Hii, R. A. Zaki, N. Aghamohammadi, and J. Rocklöv, “Research on Climate and Dengue in Malaysia: A Systematic Review,” Current Environmental Health Reports, Vol. 3, No. 1, pp. 81–90, 2016. DOI: https://doi.org/10.1007/s40572-016-0078-z
S. Das and A. Thakral, “Predictive Analysis of Dengue and Malaria,” Proceedings of the International Conference on Computing, Communication and Automation (ICCCA 2016), pp. 172–176, 2016. DOI: https://doi.org/10.1109/CCAA.2016.7813712
P. N. Hoang, J. Daniel Zucker, M. Choisy, and H. T. Vinh, “Causality Analysis Between Climatic Factors and Dengue Fever Using the Granger Causality,” Proceedings of the IEEE RIVF International Conference on Computing and Communication Technologies, pp. 49–54, 2016. DOI: https://doi.org/10.1109/RIVF.2016.7800268
N. Mathur, V. S. Asirvadam, S. C. Dass, and B. S. Gill, “Generating Vulnerability Maps of Dengue Incidences for Petaling District in Malaysia,” Proceedings of the IEEE 12th International Colloquium on Signal Processing and Its Applications, pp. 227–232, 2016. DOI: https://doi.org/10.1109/CSPA.2016.7515836
D. Phung, C. Huang, S. Rutherford, C. Chu, X. Wang, M. Nguyen, N. H. Nguyen, and C. Do Manh, “Identification of the Prediction Model for Dengue Incidence in Can Tho City, a Mekong Delta Area in Vietnam,” Acta Tropica, Vol. 141, pp. 88–96, 2015. DOI: https://doi.org/10.1016/j.actatropica.2014.10.005
S. Atique, S. S. Abdul, C. Y. Hsu, and T. W. Chuang, “Meteorological Influences on Dengue Transmission in Pakistan,” Asian Pacific Journal of Tropical Medicine, Vol. 9, No. 10, pp. 954–961, 2016. DOI: https://doi.org/10.1016/j.apjtm.2016.07.033
V. S. H. Rao and M. N. Kumar, “A New Intelligence-Based Approach for Computer-Aided Diagnosis of Dengue Fever,” IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 1, pp. 112–118, 2012. DOI: https://doi.org/10.1109/TITB.2011.2171978
N. Mohd Zainee and K. Chellappan, “A Preliminary Dengue Fever Prediction Model Based on Vital Signs and Blood Profile,” in IECBES 2016—IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp. 652–656, 2017. DOI: https://doi.org/10.1109/IECBES.2016.7843530
J. A. Potts, R. V. Gibbons, A. L. Rothman, A. Srikiatkhachorn, S. J. Thomas, P. on Supradish, S. C. Lemon, D. H. Libraty, S. Green, and S. Kalayanarooj, “Prediction of Dengue Disease Severity Among Pediatric Thai Patients Using Early Clinical Laboratory Indicators,” PLoS Neglected Tropical Diseases, Vol. 4, No. 8, pp. 2–9, 2010. DOI: https://doi.org/10.1371/journal.pntd.0000769
Z. T. Salim, U. Hashim, M. K. M. Arshad, M. A. Fakhri, and E. T. Salim, “Frequency-Based Detection of Female Aedes Mosquito Using Surface Acoustic Wave Technology: Early Prevention of Dengue Fever,” Microelectronic Engineering, Vol. 179, pp. 83–90, 2017. DOI: https://doi.org/10.1016/j.mee.2017.04.016
J.-J. Tsai, K. Chokephaibulkit, P.-C. Chen, L.-T. Liu, H.-M. Hsiao, Y.-C. Lo, and G. C. Perng, “Role of Cognitive Parameters in Dengue Hemorrhagic Fever and Dengue Shock Syndrome,” Journal of Biomedical Science, Vol. 20, No. 1, p. 88, 2013. DOI: https://doi.org/10.1186/1423-0127-20-88
S. Nubenthan and C. Shalomy, “A Wireless Continuous Patient Monitoring System for Dengue: Wi-Mon,” in Proceedings of the 6th National Conference on Technology and Management, pp. 23–27, 2017. DOI: https://doi.org/10.1109/NCTM.2017.7872822
A. Redondi, M. Chirico, L. Borsani, M. Cesana, and M. Tagliasacchi, “An Integrated System Based on Wireless Sensor Networks for Patient Monitoring, Localization and Tracking,” Ad Hoc Networks, Vol. 11, No. 1, pp. 39–53, 2013. DOI: https://doi.org/10.1016/j.adhoc.2012.04.006
F. C. J. González, O. O. V. Villegas, D. E. T. Ramírez, V. G. C. Sánchez, and H. O. Domínguez, “Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality,” Sensors, Vol. 14, No. 9, pp. 17212–17234, 2014. DOI: https://doi.org/10.3390/s140917212
S. K. Sood and I. Mahajan, “Wearable IoT Sensor-Based Healthcare System for Identifying and Controlling Chikungunya Virus,” Computers in Industry, Vol. 91, pp. 33–44, 2017. DOI: https://doi.org/10.1016/j.compind.2017.05.006
P. Rani, V. Raychoudhury, S. S. Sandha, and D. Patel, “Mobile Health Application for Early Disease Outbreak-Period Detection,” in IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), 2014. DOI: https://doi.org/10.1109/HealthCom.2014.7001890
N. R. Pandya, “Wireless Sensor Based Handy Patient Monitoring System,” in 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 634–638, 2016. DOI: https://doi.org/10.1109/IACC.2016.125
H. Rallapalli and P. Bethelli, “IoT Based Patient Monitoring System,” International Journal of Computing, Communications & Instrumentation Engineering, Vol. 4, No. 1, pp. 115–118, 2017. DOI: https://doi.org/10.15242/IJCCIE.H1216012
C. D. A. Marques-Toledo, C. M. Degener, L. Vinhal, G. Coelho, W. Meira, C. T. Codec, and M. M. Teixeira, “Dengue Prediction by the Web: Tweets Are a Useful Tool for Estimating and Forecasting Dengue at Country and City Level,” PLoS Neglected Tropical Diseases, Vol. 11, No. 7, pp. 1–20, 2017. DOI: https://doi.org/10.1371/journal.pntd.0005729
N. Kerdprasop and K. Kerdprasop, “Remote Sensing Based Modeling of Dengue Outbreak with Regression and Binning Classification,” in 2nd IEEE International Conference on Computer and Communications, 2016. DOI: https://doi.org/10.1109/CompComm.2016.7924662
S. S. Mathulamuthu, V. S. Asirvadam, S. C. Dass, and B. S. Gill, “Cluster Based Regression Model on Dengue Incidence Using Dual Climate Variables,” IEEE Conference on Systems, Process and Control, Dec. 16–18, 2016, Melaka, Malaysia. DOI: https://doi.org/10.1109/SPC.2016.7920705
G. Zhu, J. Hunter, and Y. Jiang, “Improved Prediction of Dengue Outbreak Using the Delay Permutation Entropy,” Proceedings of the IEEE International Conference on IoT, pp. 828–832, 2016. DOI: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.172
W. Anggraeni and L. Aristiani, “Using Google Trend Data in Forecasting Number of Dengue Fever Cases with ARIMAX Method: Case Study—Surabaya, Indonesia,” Proceedings of the 2016 International Conference on Information and Communication Technology and Systems, pp. 114–118, 2017. DOI: https://doi.org/10.1109/ICTS.2016.7910283
E. L. Estallo, E. M. Benitez, M. A. Lanfri, C. M. Scavuzzo, and W. R. Almiron, “MODIS Environmental Data to Assess Chikungunya, Dengue, and Zika Diseases Through Aedes (Stegomia) aegypti Oviposition Activity Estimation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 99, pp. 5461–5466, 2016. DOI: https://doi.org/10.1109/JSTARS.2016.2604577
C. Wu and P. J. Y. Wong, “Estimation of Reproduction Number of Dengue Transmission in a Partially Susceptible Population,” International Conference on Control, Automation, Robotics and Vision, 2017. DOI: https://doi.org/10.1109/ICARCV.2016.7838856
J. Albinati, W. Meira, and G. L. Pappa, “An Accurate Gaussian Process-Based Early Warning System for Dengue Fever,” Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2017. DOI: https://doi.org/10.1109/BRACIS.2016.019
N. S. Roslan, Z. A. Latif, and N. C. Dom, “Dengue Cases Distribution Based on Land Surface Temperature and Elevation,” 7th IEEE Control and System Graduate Research, Aug. 2017. DOI: https://doi.org/10.1109/ICSGRC.2016.7813307
H. I. Datoc, R. Caparas, and J. Caro, “Forecasting and Data Visualization of Dengue Spread in the Philippine Visayas Island Group,” International Conference on Info Intelligence, Systems and Applications, 2016. DOI: https://doi.org/10.1109/IISA.2016.7785420
D. Rahmawati and Y. P. Huang, “Using C-Support Vector Classification to Forecast Dengue Fever Epidemics in Taiwan,” IEEE International Conference on System Science and Engineering, 2016. DOI: https://doi.org/10.1109/ICSSE.2016.7551552
Z. A. Latif and M. H. Mohamad, “Mapping of Dengue Outbreak Distribution Using Spatial Statistics and Geographical Information System,” Proceedings of the International Conference on Information Science and Security, 2015. DOI: https://doi.org/10.1109/ICISSEC.2015.7371016
J. Xiang, A. Hansen, Q. Liu, X. Liu, M. X. Tong, Y. Sun, S. Cameron, S. Hanson-Easey, G. S. Han, C. Williams, P. Weinstein, and P. Bi, “Association Between Dengue Fever Incidence and Meteorological Factors in Guangzhou, China, 2005–2014,” Environmental Research, Vol. 153, pp. 17–26, 2017. DOI: https://doi.org/10.1016/j.envres.2016.11.009
A. E. Laureano-Rosario, J. E. Garcia-Rejon, S. Gomez-Carro, J. A. Farfan-Ale, and F. E. Muller-Karger, “Modelling Dengue Fever Risk in the State of Yucatan, Mexico Using Regional-Scale Satellite-Derived Sea Surface Temperature,” Acta Tropica, Vol. 172, pp. 50–57, 2017. DOI: https://doi.org/10.1016/j.actatropica.2017.04.017
M. N. Karim, S. U. Munshi, N. Anwar, and M. S. Alam, “Climatic Factors Influencing Dengue Cases in Dhaka City: A Model for Dengue Prediction,” Indian Journal of Medical Research, Vol. 136, No. 1, pp. 32–39, 2012.
A. L. Buczak, P. T. Koshute, S. M. Babin, B. H. Feighner, and S. H. Lewis, “A Data-Driven Epidemiological Prediction Method for Dengue Outbreaks Using Local and Remote Sensing Data,” BMC Medical Informatics and Decision Making, Vol. 12, p. 124, 2012. DOI: https://doi.org/10.1186/1472-6947-12-124
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Noor Hafizah Hassan, Ely Salwana, Sulfeeza Md. Drus, Nurazean Maarop, Narayana Samy, Noor Azurati Ahmad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
