Revolutionising health monitoring: IOT-Based system with machine learning classification
Alka Mishra et al.
What the paper says
In the pursuit of revolutionising health monitoring, this study introduces an IoT-based smart health monitoring system coupled with a machine learning classification framework. This innovative system tracks five crucial health parameters - Temperature, SPO2, Glucose level, Pulse rate, and Heart rate - providing a comprehensive overview of an individual's health status in real-time. Leveraging these parameters, a dataset is constructed, facilitating the application of four distinct machine learning algorithms: Support Vector Machine (SVM), Decision Tree, Random Forest, and CN2 rule induction. Remarkably, the classification accuracy achieved by these models demonstrates their efficacy, with SVM scoring 0.859, Tree achieving 0.996, Random Forest attaining 0.984, and CN2 rule induction reaching 0.902, respectively. Notably, among these algorithms, the Tree model emerges as the most superior, showcasing its potential for effectively analysing this type of dataset and enhancing the performance of health monitoring systems. Further, ThingSpeak has been utilised as IoT platform within our health monitoring system that facilitates the seamless collection of real-time data from diverse medical devices such as heart rate monitors and glucose metres. With applications in healthcare, home monitoring, sports, fitness, and industrial safety, the system offers versatile solutions for proactive health management and improved well-being.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.