Enhancing Elderly Health Monitoring Framework With Quantum-Assisted Machine Learning Models as Micro Services

Abstract

Monitoring systems for the elderly gather a variety of information, including blood pressure, insulin level, oxygen saturation, and more. Machine learning is a multidisciplinary method for identifying patterns in data by applying mathematical algorithms and iterative computing processes. Machine learning models are implemented as microservice-based architecture, which makes code components more maintainable, testable, and of course, responsive. The supervised model, unsupervised model, and reinforcement model are the three machine learning models that are employed as micro-services independently. This study focuses on blood sugar level among other indicators used to monitor older people, because it is the primary factor determining how well each organ functions. In this work, the machine learning model is enhanced with quantum variationally algorithm to improve their efficiency and accuracy. With an accuracy rate of 81%, the quantum assisted unsupervised model performed better than the other two models when it was being executed.