Multi-modal IoT-based medical data processing for disease diagnosis using Heuristic-derived deep learning

•To design an IoT-enabled deep learning strategy in the healthcare system.•To adopt a Mating Probability-based Hybrid Strider GSO (MP-HSGSO) approach.•Chosen optimal features using MP-HSGSO for the diagnosis of diseases using IoT.•To design an optimization algorithm named as MP-HSGSO to optimize the parameters.•To adopt a Radial Basis Recurrent Neural Network (RBRNN) for final classification. Nowadays, the quick development of the Internet of Things (IoT) has changed our lifestyle. However, disease diagnosis is a difficult task owing to managing an enormous volume of data. Thus, in this research task, the latest IoT-assisted disease detection model for healthcare applications is developed. Initially, the patient data are taken from the IoT sensor devices in image, text, and signal data. The gathered patient data is further forward to the pre-processing phase. Accordingly, the images are gathered from patients’ data using IoT devices and forward to the feature extraction process. Feature extraction is done based on the IoT-collected images using VGG16 and Inception network. Moreover, the signals are garnered from the patient’s data and fed to the decomposition procedure. It is done by Empirical Mode Decomposition (EMD) technique. Then, the optimal feature selection of data, images, and signals is carried out by a hybrid algorithm termed Mating Probability-based Hybrid Strider Glowworm Swarm Optimization (MP-HSGSO). Finally, the Radial Basis Recurrent Neural Network (RBRNN) using MP-HSGSO is suggested to get the detected outcomes. Here, various brain diseases have been detected to classify the disease. This designed RBRNN method is utilized for efficient treatment, and also it provides the appropriate decision-making through processing various kinds of multimodal data. Throughout the simulation outcomes, the accuracy and precision of the offered method achieve 97 % and 96 %. Consequently, the simulation outcome of the offered method is proved that the designed model using a deep learning model shows enriched performance in clinical trial applications.