A Meta heuristic based deep learning classifier for effective dengue disease prediction in IoT‐Fog system

Abstract

The primary aim of this article is to propose an effective dengue disease monitoring system by integrating deep learning models with Internet of Things (IoT) and fog computing. In this context, the disease related parameters are collected using IoT devices and further, the reports are securely transmitted to the healthcare facilities utilizing fog computing. In this article, the misdiagnosis error is reduced utilizing attention based long short term memory (ALSTM) network with coati optimization algorithm (COA). The Attention model allocates higher weight to significant parameters and pay additional consideration while training the model. Therefore, the conventional LSTM network is combined with a self‐attention mechanism for enabling this model to focus on relevant parts of the input sequences for precise dengue disease prediction. The Attention Model differs from the conventional model since it delivers higher amount of data into the decoder and improves additional stages to the attention decoder before making its output. Furthermore, the COA is integrated with the ALSTM network for optimal hyper‐parameter selection. The ALSTM‐COA model evaluates the dengue related parameters like nausea, vomiting, skin rash, joint pain, muscle pain, soft bleeding, temperature of water‐sites, humidity of water‐sites, and so forth, for timely dengue disease diagnosis and clinical decision‐making. The experiments performed on a real time dataset state that the ALSTM‐COA model achieves significant prediction results by utilizing different performance measures like recall, accuracy, fall‐out, miss rate, training time, and testing time. The ALSTM‐COA model obtains an accuracy of 99.27%, latency of 0.03 s, and time complexity of 8 s for 500 health records, which are superior to the comparative deep learning models.