Health Monitoring IoT Device with Risk Prediction using Cloud Computing and Machine Learning

Abstract

The thesis is carried out in complete compliance with research ethics norms, and the codes and practices set by BRAC University. In our thesis, we use the data from primary sources. We collect data from different participants, and we use our own dataset for our thesis. We are ensuring we use references and in-text citations properly. We the four co-authors take full responsibility for the thesis code violations. For solving problems, we read different websites, YouTube tutorials, and Questionnaire Free tools. We also took help from our university faculty members. Finally, we declare that we give credit to every people from whom we took help. We did not make any fraud able means for completing the thesis. Our work is in compliance with the ethics standard set by BRAC University.

Advanced Machine Learning model for Health Monitoring in IoT System

“…The study's key contribution is the ability to discern between static and dynamic persons, as well as the ability to save these assessments throughout time. A. Das, et al [3] The construction of an IoT system that connects a number of sensors to a microsystem and sends the collected data to a cloud server for machine learning processing. If the doctor believes the patient's health is at risk as a result of the test, he or she may use our proposed device to send a risk report to the patient.…”

Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review

“…IoT collects remote patient health data using wearable sensors and devices connected to an internet-based health monitoring system [3,4]. The IoT has been a primary driver of globalization and has promoted the convergence of electronic communications and media services that can be leveraged to monitor the patient's condition from anywhere for successive feasibility of all associated stakeholders [5][6][7].…”

“…Previous research in the field of IoT technology and its applications in healthcare, patient monitoring, and pregnancy monitoring has exhibited several limitations, including data security and privacy in their IoT health monitoring equipment, confronting issues in sensor accuracy, and facing the continual issue of maintaining security across all layers of IoT, from perception to application layers, to handle increasing security threats [2][3][4][5][6][7]. There is a lack of comprehensive systematic reviews in these domains, with previous studies failing to synthesize existing knowledge effectively.…”