An Innovative DOS-Care system using Boosted Binary Harris Hawks Optimization and Gated Recurrent Deep Convolutional Network Classification Mechanisms
Keywords:
Internet of Things (IoT), Smart Healthcare, Disease Diagnosis, Diabetes, Heart Disease, Feature Optimization, Deep LearningAbstract
In the smart healthcare industry, the Internet of Things (IoT) is frequently used to identify various ailments. It has been observed that the manual detection method takes a long time and may produce detection errors that affect the diagnosis. As a result, an autonomous system is required, which is why deep learning techniques are of great importance. As a result, the concept of fusing illness prediction with Deep Learning (DL) to effectively predict the disease is started. In this study, a revolutionary framework for the detection of diabetes and heart disease using medical data is created. It is known as the Deep Optimized Smart Healthcare (DOS-Care) system. Here, a sophisticated Boosted Binary Harris Hawks (BB-HH) optimization technique is used to lower the dataset's dimensionality in order to accelerate and enhance the performance of the classifier as a whole. Following that, the BB-HH optimization model's features are used to develop the Gated Recurrent Deep Convolutional Network (GRDCNet) technique to classify the type of disease. Additionally, loss function of the GRDCNet is estimated using the Krill Herd Optimization (KHO) technique, enhancing the accuracy of the classifier. The Cleveland, Alizadeh, and PIMA-based heart disease and diabetes datasets, which are the most widely used in this work for performance assessment and validation.
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