An IoT-Based Treatment Optimization and Priority Assignment Using Machine Learning (original) (raw)
1938-5862/107/1/1487
Abstract
In India, the resources availability with respect to demand has a larger gap in reality. For data analytics and resource scheduling for optimized access using the Internet of Things (IoT) as a data frontend collector, a machine learning technique is discussed in this paper. Telemedicine was initially considered "futuristic" and "experimental," but telemedicine is the need of this time. Telemedicine applies in a number of fields: patient care, education, science, management, and public health. The present study describes launchpad's for patient compliance. The proposed approach is aimed at achieving an integrated IoT Unit, Machine Learning Unit, and Broadcasting Unit treatment platform using an interface medium. Methods: The proposed system is designed for data collection using MSP430 and RL200 Launchpads at the patient side under sensory operations. The processed data is extracted to servers for processing and thus providing an optimized machine learning technique for resource sharing, such as drug distribution, instrumental enhancement, and appointment allocation. Novelty: The proposed system architecture utilizes user group from various locations under both rural and urban categories installing the monitoring application as discussed under the IoT unit of for data acquisition and transmission. The hardware kit is aided with MSP430 and supporting Wi-Fi for connectivity to cloud servers. Findings: In the future stage of experiment and setup, MSP430 was replaced by RL200 with a built-in Wi-Fi module for easy connectivity and achieve compactness. The proposed technique has achieved satisfactory results for open datasets under the Hadoop environment.