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Mathematics
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Artificial Intelligence Solutions in Healthcare
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Artificial Intelligence Solutions in Healthcare

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: 2 September 2024 | Viewed by 1313

Special Issue Editors

Dr. Ivan Lorencin

E-Mail Website
Guest Editor
1. Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
2. METRIS Research Center, Istrian University of Applied Sciences, 52100 Pula, Croatia
Interests: artificial intelligence; machine learning; intelligent control systems; computer vision; evolutionary robotics
Dr. Nikola Anđelić

E-Mail Website
Guest Editor
Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
Interests: applied artificial intelligence; machine learning; molecular dynamics; non-local theory

Special Issue Information

Dear Colleagues,

Today, artificial intelligence is an unavoidable part of everyday life. It can be used in a wide range of activities, from tourism to sports. One of the fields where the application of artificial intelligence shows significant room for improvement is certainly healthcare. This Special Issue is intended for the publication of research and review papers dealing with the application of artificial intelligence in healthcare. Furthermore, the public publication and description of new healthcare datasets is encouraged.

Dr. Ivan Lorencin
Dr. Nikola Anđelić
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • data sets
  • healthcare
  • machine learning
  • medicine

Published Papers (1 paper)

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Research

29 pages, 2614 KiB  
Article
Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases
by Fahad Alqurashi, Aasim Zafar, Asif Irshad Khan, Abdulmohsen Almalawi, Md Mottahir Alam and Rezaul Azim
Mathematics 2023, 11(22), 4621; https://doi.org/10.3390/math11224621 - 11 Nov 2023
Viewed by 875
Abstract
Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) [...] Read more.
Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors. Full article
(This article belongs to the Special Issue Artificial Intelligence Solutions in Healthcare)
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