Future of Information and Communication Conference (FICC) 2024
4-5 April 2024
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: In the realm of Internet of Things (IoT)-driven healthcare, diverse technologies, including wearable medical devices, mobile applications, and cloud-based health systems, generate substantial data streams, posing challenges in real-time operations, especially during emergencies. This study recommends a hybrid architecture utilizing Hadoop for real-time processing of extensive medical data within the IoT framework. By employing distributed machine learning models, the system analyzes health-related data streams ingested into Spark streams via Kafka threads, aiming to transform conventional machine learning methodologies within Spark's real-time processing, crafting scalable and efficient distributed approaches for predicting health statuses related to diabetes and heart disease while navigating the landscape of big data. Furthermore, the system provides real-time health status forecasts based on a multitude of input features, disseminates alert messages to caregivers, and stores this valuable information within a distributed database, which is instrumental in health data analysis and the production of flow reports. We compute a range of evaluation parameters to evaluate the proposed methods' efficacy. This assessment phase encompasses measuring the performance of the Spark-based machine learning algorithm in a distributed parallel computing environment.
Yu Lina and Su Wenlong, “Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150108
@article{Lina2024,
title = {Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150108},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150108},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Yu Lina and Su Wenlong}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.