CDIPEM: Cardiovascular disease incidence probability estimation CNN model

Bodiwala, Sunny; Nanavati, Nirali

doi:10.47974/JIOS-1175

Open Access Research Article

CDIPEM: Cardiovascular disease incidence probability estimation CNN model

^*Sunny BodiwalaCorresponding authorsunny.bodiwala@gmail.comDepartment of Computer Engineering Gujarat Technological UniversityAhmedabad, Gujarat, 382424, IndiaView full profile → , Nirali NanavatiDepartment of Computer Engineering Sarvajanik College of Engineering and TechnologySurat, Gujarat, 395001, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 865–894Vol. 44Issue 5July 2023DOI: 10.47974/JIOS-1175XML

Received:: 09 Nov 2021
Accepted:: 05 May 2022
Published Online:: 05 Jul 2023
Article type:: Research Article
Language:: EN
Article no.:: JIOS-1175
Pages:: 865–894

Abstract

Heart disease affects the majority of the world’s population, with a high rate of morbidity and mortality. The prediction of heart disease at an early stage is viewed as one of the imperative issues in clinical trials. Since there is a huge amount of medical data and it keeps on increasing, it becomes very difficult to analyze and process such data. Hence, machine learning algorithms become an obvious choice to handle such data. This paper presents a classification framework for clinical decision-making that uses an optimized convolutional neural network (CNN). The pre-processed clinical dataset is used for the training and testing of the classifier. A publicly available UCI dataset is used to examine how the system performs. The statistical features are extracted at the beginning and then subsequent data minimization is carried out. Therein-after, the prediction of heart diseases is done with the help of CNN. The identification of optimal weights is done to enhance the performance of CNN which in turn gives accurate disease prediction results. In this paper, we propose a hybrid Particle Swarm Optimization (PSO) and Gray wolf Optimization (GWO) technique namely PS-GW technique to identify the optimal weight parameters. Finally, performance analysis is done and the experimental results are compared with the existing approaches which show improved classification accuracy over conventional optimization techniques and some well-known classifiers such as k-nearest neighbour, decision tree, logistic regression, naive bayes and random forest.

Keywords

Convolutional neural network Heart disease prediction Meta-heuristics Medical diagnoses

Subject Classifications

68Txx11Kxx

References

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