Hybrid deep learning framework for cardiovascular disease diagnosis and prognosis using GAN, LSTM, GRU, VARMA, and deep DynaQ network

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Effective CVD treatment requires early and accurate diagnosis. CVD diagnosis and prognosis can be done using medical image analysis. In this paper, we propose a novel deep learning approach to improve diagnosis using GAN (Generative Adversarial Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), VARMA (Vector Auto Regressive Moving Average), and Deep Dyna Q Network. GAN generates synthetic medical images to train LSTM and GRU models for sequential medical image time series data analysis. VARMA models time series dataset sample temporal dependencies. Deep Dyna Q Network is used to learn the best CVD diagnosis and treatment policy. A large dataset of medical images and patient data trains the model, which is evaluated performance metrics. The proposed approach outperforms state-of-the-art CVD diagnosis methods in clinical scenarios, achieving high accuracy 95% and sensitivity. The proposed deep learning approach has great potential to improve diagnosis and treatment, cardiovascular medication, and patient lifespan in real-time scenarios.