Probabilistic Neural Network Based Visual Data Mining for Healthcare Sector
Ali S. Abosinnee, Samer Saeed Issa, Ahmed Hussein Alkhayyat, Zamen Latef Naser, Ghazi Mohamad Ramadan, Zain Jaffer · 2023
Data mining is a process that interacts with a large dataset to determine complex, interesting patterns from unknown structured data. Visual data mining (VDM) is considered a combination of two disciplines: data mining and visualization, to explore useful and implicit knowledge from a huge dataset. Also, it is strongly associated with high-performance computing, computer graphics, multimedia systems, human-computer interaction, and pattern recognition. Recently, VDM approaches have been useful in the healthcare sector to aid decision-making. In addition, the design of VDM approaches for health care applications needs special consideration to ensure secure data. Artificial intelligence (AI) methods are vital in achieving scalability and accurate analysis from real-time environments. Therefore, this study develops a blockchain-assisted quantum bacterial colonial optimization with deep learning (BAQBCO-DL) algorithm for VDM in the healthcare environment. The proposed BAQBCO-DL model exploits BC technology for secured data communication in the healthcare sector. In addition, the BAQBCO-DL model designs a U-Net-based segmentation with an optimal densely connected network (DenseNet) model for feature extraction. The hyperparameter tuning process is performed using the QBCO algorithm. Finally, a probabilistic neural network (PNN) classifier is utilized to determine proper class labels. A wide-ranging simulation analysis is carried out to report the enhanced performance of the BAQBCO-DL model, and the outcomes are assessed under several aspects. The simulation outcomes highlighted the supremacy of the BAQBCO-DL model over recent methodologies.