Intelligent Medical Service Monitoring Health Care System for the Elderly

The challenges posed by a growing elderly population in healthcare and social care have resulted in significant focus on ambient assisted living from both scholars and businesses. The governing body has emphasised the importance of utilising specialised knowledge in the development, execution, and verification of solutions. They also recognise the crucial role that technology plays in their efforts to manage or reduce healthcare expenses while improving the quality of service. This study proposes the implementation of an intelligent healthcare monitoring system for senior individuals. The system would allow remote real-time monitoring with the objective of identifying and addressing ongoing health issues promptly. The system aims to detect potential ailments and support early intervention efforts by exploring cutting-edge technology for monitoring physiological data. It is crucial to process and interpret sensory data accurately and promptly notify the relevant healthcare professionals of any concerning findings. The results suggest that this innovative approach significantly enhances clinical decision-making and expedites the implementation of early intervention techniques. Comprehensive simulations provide evidence that the proposed system surpasses the baseline, achieving maximum efficiency in data collection and modification, while also retaining a low rate of packet loss and low latency. The study examines a smartphone application designed for senior citizens that utilises automated contacts and emergency alerts, along with machine learning, to enhance effectiveness and optimise usage. The challenges posed by a growing elderly population in healthcare and social care have resulted in significant focus on ambient assisted living from both scholars and businesses. This study proposes the implementation of a smart healthcare monitoring system (SW-SHMS) specifically designed for senior adults. The system allows for remote monitoring in real-time, with the primary objective of promptly identifying health issues and permitting immediate care. SW-SHMS employs wearable sensors to gather physiological data, which is then transmitted to the cloud for processing and analysis utilising machine learning methodologies. The proposed feedforward neural network model achieved a detection accuracy of 97.64% for diabetes, surpassing the accuracy of KNN (78.57%) and Naive Bayes (76.46%). The neural network proposed for cardiovascular illness diagnosis achieved an accuracy of 99.67%, surpassing the accuracy of 83.77% for KNN and 82.56% for Naive Bayes. The results suggest that this innovative approach significantly enhances clinical decision-making and expedites early intervention methods. The study also examines a smartphone application designed for senior citizens that utilises automated medication monitoring and emergency notifications. The system’s data collection and modification efficiency is demonstrated through extensive simulations, with low packet loss and latency being maintained.