A Hybrid Deep Learning Approach for Predicting Patient Health Outcomes in Mobile Healthcare Applications
Keywords:
Hybrid Deep Learning, Patient Health Prediction, Mobile Healthcare Applications, CNN, LSTM, Attention Mechanism, Real-Time Health Monitoring, Predictive Analytics,Abstract
Along with mobile health care apps, deep learning has transformed health monitoring and prediction. A hybrid approach based on deep learning for mobile health systems for precise patient health outcome prediction is proposed in this paper. It exploits Convolutional Neural Networks (CNN) to extract the features followed by Long Short Term Memory (LSTM) networks to learn from the sequential pattern for efficient analysis of the patients' vitals, past medical history and real-time sensor data. Also Attention Mechanism plays very significant role in highlighting important health parameters thus interprets and explains levels of data which helps in decision improvement through the model. We train the hybrid model on heterogeneous healthcare data and test it with accuracy, precision, recall and F1-score. The experimental results demonstrate significant benefits in terms of predictive consistency and real-time flexibility than traditional deep learning models. This framework could change the base of mobile healthcare applications to initiate early disease detection, personal treatment recommendations, and timely involvement in the patient journey that would facilitate healthier and more effective healthcare.
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