IoT Health Science Data Analytics Model for the Prevalence of Anxiety and Depression in Working Professionals
DOI:
https://doi.org/10.69996/jsihs.2024008Keywords:
Data Analytics, Internet of Things (IoT), Healthcare, Re-enforcement Learning, ClassificationAbstract
This paper investigates the impact of data analytics in health science, particularly focusing on the integration of Internet of Things (IoT) technologies for mental health management. Utilizing various IoT data sources—wearable devices, environmental sensors, and healthcare monitors the study highlights their role in real-time monitoring of physiological metrics, environmental conditions, and societal trends. For instance, wearable devices capture heart rate and activity data, facilitating continuous health assessment. Environmental sensors monitor air quality and temperature, critical for assessing environmental impact. Evaluation of machine learning models such as Gradient Boosting, Convolutional Neural Networks (CNNs), Ensemble Learning, and XGBoost demonstrates their effectiveness in classifying health data with high accuracy. Gradient Boosting achieves 92.5% accuracy, with precision and recall both exceeding 92%. CNNs follow closely with 91.8% accuracy and balanced precision and recall metrics. Ensemble Learning and XGBoost also perform strongly, each achieving over 90% accuracy.
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