A Hybrid Deep Learning Framework for Accurate and Early Health Risk Assessment

In preventive health care and better patient out­comes, early and correct health risk assessment is of primary importance. Traditional machine learning tools are not always useful in capturing spatial and temporal relationships on complex medical data. The article is a review and discussion of hybrid deep learning models that comprise convolutional neural net­works (CNNs) and recurrent models (Long Short-Term Memory (LSTM)). We explore 30 state-of-the-art papers that include cardiovascular disease diagnosis, diabetes diagnosis, respiratory disease diagnosis and multi-disease risk diagnosis at a systematic level. Our analysis reveals that hybrid CNN-LSTM models are always more effective, with accuracy rates of 92.5 to 98.26 with the can-reach of up to 0.98 in all types of healthcare contexts. The principal architectural trends, benchmarks, commonly utilized datasets, and the gaps in the research that most address the gaps that we are interested in identifying are the real-world generalizability, interpretability, cross-domain robustness. This work provides a convenient starting point to scientists and medical personnel who have to innovate in the next generation of health risk assessment system.