Novel Algorithm of CPU-GPU hybrid system for health care data classification
Due to advancements in portable health monitoring technology, such systems have become more and more economical & efficient. This in turn has resulted in a huge amount of data being generated every moment by millions of users of such portable devices. Such voluminous data may include audio, video, and image, and text representing blood pressure, temperature, vocal activity, ECG, sugar level etc. In the Proposed algorithm, first step is assignment, where clusters are assigned to a patient data and the second step is update, which takes the mean of the coordinates of all the data in its cluster. Medical practitioners and service providers can use such data to discover various patterns and useful insights. Such insights can be very useful on understanding various trends during epidemics, such as Malaria, Dengue, Chikungunya and other such outbreaks. A faster and economical way to get such insights is of paramount importance.
Keywords: health monitoring; GPU; ECG; epidemics; data mining
 Reyes-Ortiz J.-L., Oneto L, Sama A, Parra X, Anguita D, “Transition-aware human activity recognition using smartphones,” Neurocomputing, vol. 171, pp. 754–767, 2016.
 Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP, “Machine learning for Internet of Things data analysis: A survey,” Digit. Commun. Netw. 2017.
 Patel S, Park H, Bonato P, Chan L, Rodgers M, A review of wearable sensors and systems with application in rehabilitation, Journal of NeuroEngineering and Rehabilitation, 2012.
 Zheng J, Zhang Z, Wu T, Zhang G, Emerging Wearable Medical Devices towards Personalized Healthcare , Conference: Proceedings of the 8th International Conference on Body Area Networks, September 2013.
 Tanweer S, Mobin A, Alam A, “Environmental Noise Classification using LDA, QDA and ANN Methods”, Indian Journal of Science and Technology, 2016; 9(33) , September. ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645
 Singh I, Shriraman A, WL Fung W, O'Connor M, Aamodt TM, Cache coherence for GPU architectures, IEEE 19th International Symposium on High Performance Computer Architecture (HPCA2013), 2013.
 Haraty RA, Dimishkieh M, Masud M, An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data, International Journal of Distributed Sensor Networks vol. 11,June 2015.
 Analysis of K-Means and K-Medoids Algorithm For Big Data, Procedia Computer Science, Volume 78, 2016, Pages 507-512 in 1st International Conference on Information Security & Privacy, 2015.
 Bottou L., Tesauro BY, Touretzky D. Convergence properties of the k-means algorithms Advances in Neural Information Processing Systems New York, NY, USAMIT,1995.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).