Article: Diagnosing cardiovascular disease via intelligence in healthcare multimedia: a novel approach Journal: International Journal of Nanotechnology (IJNT) 2023 Vol.20 No.1/2/3/4 pp.182 - 198 Abstract: Diagnosing cardiovascular disease (CVD) in its early stages remains a challenge despite the existence of all medical technologies and devices that are being used. Besides the digitised form of collecting and organising data, prediction and diagnosis are two stumbling blocks in CVD. This study explores statistical machine learning models with a multimedia health care approach using AI to predict risk factors of heart diseases associated with type 2 diabetes mellitus (T2DM). This study investigates an efficacy of a mathematical model to perform attribute evaluation using information criteria-based selection in LASSO regression. The present study implements the deep learning algorithm using a multilayer perceptron (MLP) classifier with Gaussian process classification (GPC) that provides probabilistic predictions in terms of linear and non-linear functions. The performance of the classifier is evaluated using precision, recall and accuracy metrics. The proposed classification model yields 93.59% accuracy of 10 cross-validations assorted with sigmoid function for better analysis. Inderscience Publishers - linking academia, business and industry through research

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Title: Diagnosing cardiovascular disease via intelligence in healthcare multimedia: a novel approach

Authors: Geeitha Senthilkumar; Fadi Al-Turjman; Rajagopal Kumar; Jothilakshmi Ramakrishnan

Addresses: Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Thalavapalayam, 639113, Tamil Nadu, India ' Faculty of Engineering, Research Center for AI and IoT, University of Kyrenia, Mersin 10, Turkey; Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Nicosia, Mersin 10, Turkey ' Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland-797103, India ' Department of Mathematics, Mazharul Uloom College Ambur, Ambur, 635802, Tamil Nadu, India

Abstract: Diagnosing cardiovascular disease (CVD) in its early stages remains a challenge despite the existence of all medical technologies and devices that are being used. Besides the digitised form of collecting and organising data, prediction and diagnosis are two stumbling blocks in CVD. This study explores statistical machine learning models with a multimedia health care approach using AI to predict risk factors of heart diseases associated with type 2 diabetes mellitus (T2DM). This study investigates an efficacy of a mathematical model to perform attribute evaluation using information criteria-based selection in LASSO regression. The present study implements the deep learning algorithm using a multilayer perceptron (MLP) classifier with Gaussian process classification (GPC) that provides probabilistic predictions in terms of linear and non-linear functions. The performance of the classifier is evaluated using precision, recall and accuracy metrics. The proposed classification model yields 93.59% accuracy of 10 cross-validations assorted with sigmoid function for better analysis.

Keywords: AI; artificial intelligence; CVD; cardiovascular disease; multimedia health care; feature selection; T2DM; type 2 diabetes mellitus.

DOI: 10.1504/IJNT.2023.131110

International Journal of Nanotechnology, 2023 Vol.20 No.1/2/3/4, pp.182 - 198

Received: 20 Jan 2021
Accepted: 02 Jun 2021
Published online: 31 May 2023 *

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Title: Diagnosing cardiovascular disease via intelligence in healthcare multimedia: a novel approach

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