Predicting Cardiac Health Conditions Using IOT-Enabled Deep Ensemble Learning Systems

Abstract

The ongoing evolution of the Internet of Things (IoT) has significant implications for health technology, offering new avenues to promote healthier lifestyles through advanced data analytics. In particular, individuals with chronic illnesses stand to benefit from improved monitoring and forecasting capabilities. Traditional healthcare systems often struggle to provide real-time insights and predictive capabilities for chronic conditions, leading to delays in intervention and increased healthcare costs. There is a pressing need for a more affordable and effective solution that integrates advanced technologies to enhance patient care. This paper introduces a novel system that integrates machine learning with IoT to analyze long-term health data. The system utilizes deep residual learning for feature extraction and Fruit Fly Optimization for classification, enabling accurate forecasting of illness progression. The proposed prototype is designed to leverage big data architecture and artificial intelligence to provide actionable insights and support proactive healthcare. The system was evaluated using a dataset of over 10,000 health records. The deep residual learning model achieved an accuracy of 92.5% in feature extraction, while the Fruit Fly Optimization-based classification yielded a precision of 89.3%. The system demonstrated a 15% improvement in forecasting accuracy compared to traditional methods. These results highlight the potential for the system to provide more reliable and timely predictions, reducing healthcare costs and improving patient outcomes.