Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

Abstract

The concept of smart healthcare has seen a gradual increase with the expansion of information technology. Smart healthcare will use a new generation of information technologies, like artificial intelligence, the Internet of Things (IoT), cloud computing, and big data, to transform the conventional medical system in an all-around way, making healthcare highly effective, more personalized, and more convenient. This work designs a new Heap Based Optimization with Deep Quantum Neural Network (HBO-DQNN) model for decision-making in smart healthcare applications. The presented HBO-DQNN model majorly focuses on identifying and classifying healthcare data. In the presented HBO-DQNN model, three stages of operations were performed. Data normalization is applied to pre-process the input data at the initial stage. Next, the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data. At last, the DQNN model is exploited for healthcare data classification. A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model. The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05% and 95.72% under the colon cancer and lymphoma dataset.