A Multi‐Modal Healthcare Data Prediction Model With Fusion of Multi‐Scale Dilated RAN With Adaptive Hybrid Deep Learning Using Improved Optimization Algorithm

ABSTRACT The advancement of digital technologies is used for providing more enhanced healthcare services to patients in a timely and effective manner. The multi‐modal data encompasses a huge amount of information when compared to the single‐modal data. Fusing and analyzing various data types provides a more comprehensive understanding of the patient's condition. Fusing these multi‐modal data poses several technical challenges because of its data incompatibility. Therefore, this research work focuses on implementing a deep learning‐based disease prediction model using multi‐modal data to generate precise prediction results regarding healthcare applications. Initially, the required multi‐modal data such as signal, data and image are gathered from the standardized benchmark data sources. Then, the collected data is subjected to the implemented multi‐modal data‐based disease prediction network (MMPredNet). This network is developed by combining an adaptive hybrid deep learning network (AHDLN) and a multi‐scale dilated residual attention network (MDRAN). Here, MDRAN performs a feature extraction process to extract the features from the input data. further, the prediction process is carried out using the AHDLN model. It is a hybridized network generated by fusing a deep Bayesian network (DBN) with a deep shallow network (DSN). The parameters of the AHDLN are optimized using the adaptive learning rate‐based dove swarm optimization (ALR‐DSO) algorithm to reduce FPR and enhance the precision, NPV, and accuracy of the prediction outcome. From the MMPredNet, the final disease prediction outcomes are provided. The performance of the implemented multi‐modal data processing model is evaluated with various conventional methods to showcase its effectiveness in healthcare. The accuracy of the developed model on text data is 97.31%, images are 98.02%, and the signal is 97.23%, which is enhanced than the prior works. Hence, it is proved that the developed framework can accurately predict the disease at an early stage and helps to improve patient outcomes and prevent the progression of diseases in patients.