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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: closed (2 September 2024) | Viewed by 5045

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

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Published Papers (3 papers)

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Research

18 pages, 2561 KiB  
Article
Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals
by Syed Nisar Hussain Bukhari and Kingsley A. Ogudo
Mathematics 2024, 12(10), 1575; https://doi.org/10.3390/math12101575 - 18 May 2024
Cited by 2 | Viewed by 1499
Abstract
The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, [...] Read more.
The detection of Parkinson’s disease (PD) is vital as it affects the population worldwide and decreases the quality of life. The disability and death rate due to PD is increasing at an unprecedented rate, more than any other neurological disorder. To this date, no diagnostic procedures exist for this disease. However, several computational approaches have proven successful in detecting PD at early stages, overcoming the disadvantages of traditional methods of diagnosis. In this study, a machine learning (ML) detection system based on the voice signals of PD patients is proposed. The AdaBoost classifier has been utilized to construct the model and trained on a dataset obtained from the machine learning repository of the University of California, Irvine (UCI). This dataset includes voice attributes such as time-frequency features, Mel frequency cepstral coefficients, wavelet transform features, vocal fold features, and tremor waveform quality time. The model demonstrated promising performance, achieving high accuracy, precision, recall, F1 score, and AUC score of 0.96, 0.98, 0.93, 0.95, and 0.99, respectively. Furthermore, the robustness of the proposed model is rigorously assessed through cross-validation, revealing consistent performance across all iterations. The overarching objective of this study is to contribute to the scientific community by furnishing a robust system for the detection of PD. Full article
(This article belongs to the Special Issue Artificial Intelligence Solutions in Healthcare)
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23 pages, 5804 KiB  
Article
The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis
by Najib Ur Rehman, Ivan Contreras, Aleix Beneyto and Josep Vehi
Mathematics 2024, 12(10), 1567; https://doi.org/10.3390/math12101567 - 17 May 2024
Viewed by 1365
Abstract
This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and [...] Read more.
This study investigates how missing data samples in continuous blood glucose data affect the prediction of postprandial hypoglycemia, which is crucial for diabetes management. We analyzed the impact of missing samples at different times before meals using two datasets: virtual patient data and real patient data. The study uses six commonly used machine learning models under varying conditions of missing samples, including custom and random patterns reflective of device failures and arbitrary data loss, with different levels of data removal before mealtimes. Additionally, the study explored different interpolation techniques to counter the effects of missing data samples. The research shows that missing samples generally reduce the model performance, but random forest is more robust to missing samples. The study concludes that the adverse effects of missing samples can be mitigated by leveraging complementary and informative non-point features. Consequently, our research highlights the importance of strategically handling missing data, selecting appropriate machine learning models, and considering feature types to enhance the performance of postprandial hypoglycemia predictions, thereby improving diabetes management. Full article
(This article belongs to the Special Issue Artificial Intelligence Solutions in Healthcare)
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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
Cited by 2 | Viewed by 1414
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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