Harmonizing Health: Early Detection of Hormonal Imbalances Through Smart Wearables and Ensemble Deep Learning Models

In proactive healthcare, this research explores the integration of smart wearable devices and sophisticated ensemble deep learning models for the early detection of hormonal imbalances. Our study employs multimodal data collected through smart wearables, encompassing parameters such as heart rate variability, body temperature, sleep metrics, stress levels, blood pressure, menstrual cycle tracking, and glucose levels. The data undergoes analysis through an ensemble of deep learning models, combining the strengths of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The ensemble model not only discerns intricate patterns within the physiological data but also excels at detecting subtle anomalies indicative of hormonal irregularities. Furthermore, it establishes personalized baselines for individuals, enhancing the precision of early detection. By leveraging this integrative approach, our research aims to contribute significantly to the advancement of healthcare practices, enabling timely interventions and improved patient outcomes. The proposed study employs a rich and diverse dataset collected from smart wearables, encompassing key physiological parameters. This meticulously curated dataset is utilized to train and validate ensemble deep learning models, enhancing our capacity for early detection of hormonal imbalances and advancing personalized healthcare. Harmonizing Health signifies a novel stride toward the proactive and personalized management of hormonal health, illustrating the potential of wearable technology and ensemble deep learning models in shaping the future of healthcare.