Design of IoT-Based Wearables for Health Care Prediction Using Normalized-Patch Gan Based Fruit Fly Optimization

Abstract

Healthcare is a critical sector where timely and accurate predictions can save lives and improve the quality of care. Traditional healthcare systems often lack the ability to process vast amounts of patient data efficiently. To address this, IoT technology is harnessed for seamless data collection and integration, facilitating real-time updates to a central database. The challenge lies in harnessing this data effectively to predict health conditions. The diversity of patient data, including Medical IDs, pulse rates, medical reports, and symptoms, requires sophisticated algorithms to extract meaningful insights. Moreover, the accuracy and reliability of predictions are vital to ensure patient safety. This paper presents the design of an Internet of Things (IoT)-based healthcare prediction system utilizing the Normalized Patch Generative Adversarial Network (NP-GAN) based Fruit Fly Optimization (FFO) algorithm. The proposed system aims to predict health conditions based on patient data, including Medical ID, pulse rate, medical reports, and symptoms. Through seamless integration of IoT technologies and AI algorithms, the system enables real-time monitoring and predictive analysis, enhancing patient care and medical decision-making. The system collects patient data including Medical ID, pulse rate measurements, medical reports, and reported symptoms. IoT devices facilitate real-time data transmission to the central database. Raw data undergoes preprocessing, including normalization and sequence alignment. Textual medical reports are transformed into numerical vectors using techniques like word embeddings. Features such as pulse rate trends, symptom sequences, and medical report patterns are extracted from the preprocessed data, providing valuable insights for prediction using NP-GAN. The RCNN algorithm, combining recurrent and convolutional layers, is employed for its ability to capture temporal dependencies and spatial patterns in data. The network learns to associate pulse rate trends, symptoms, and medical information for accurate predictions. The RCNN model is trained using historical patient data and validated using FFO to optimize hyperparameters and prevent overfitting. Real-time patient data is continuously fed into the trained RCNN-FFO model, which predicts potential health issues. Alerts are generated for medical professionals if anomalies or concerning patterns are detected. The system performance is assessed using metrics like accuracy, precision, recall, and F1-score. Continuous feedback and retraining improve prediction accuracy over time.