Title: Q-DenseNet for heart disease prediction in spark framework

Authors: Pendela Kanchanamala; Grandhi Siva Sankar; Karri Aruna Bhaskar

Addresses: Department of CSE, GMR Institute of Technology, Rajam, Andhra Pradesh, India ' Department of AIML, Aditya University, Surampalem, Kakinada (Dist.), Andhra Pradesh, India ' Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Abstract: This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

Keywords: fast fuzzy C-means clustering; FFCM; Q-DenseNet; quantum dilated convolutional neural network; DenseNet; quantile normalisation.

DOI: 10.1504/IJAHUC.2024.142164

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.3, pp.127 - 142

Received: 27 Jan 2024
Accepted: 08 May 2024
Published online: 10 Oct 2024 *

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