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Algorithms
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Artificial Intelligence Algorithms for Medicine (2nd Edition)
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Artificial Intelligence Algorithms for Medicine (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 2970

Special Issue Editors

Dr. Antonio Sarasa-Cabezuelo

E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28001 Madrid, Spain
Interests: machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesc Pozo

E-Mail Website
Co-Guest Editor
Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Universitat Politècnica de Catalunya (UPC), Campus Diagonal-Besòs (CDB), Eduard Maristany, 16, 08019 Barcelona, Spain
Interests: structural health monitoring; condition monitoring; piezoelectric transducers; PZT; data science; wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the Big Data phenomenon has driven the application of informatics in medicine to solve multiple problems in the field. In particular, the use of artificial intelligence algorithms, specifically machine learning algorithms, is turning out to be very useful in problems of disease prediction, the search for patterns of characteristics to identify populations at risk, the discovery of factors that influence the appearance of diseases, medical image processing and information extraction, and the classification of medical information. In this sense, a field of work has been developed that specializes in the design and application of algorithms specifically aimed at solving problems in medicine. The objective of this Special Issue is to bring together works that show the latest advances in the application of artificial intelligence algorithms in the medical field, as well as specific experiences and applications to specific problems.

The objective of this Special Issue is to serve as a meeting point for all researchers who are working in these fields both theoretically and with an applied focus. The topics of interest include, but are not limited to the following:

  • Machine learning applied to medicine;
  • Artificial intelligence applied to medicine;
  • Big Data and health;
  • The application of artificial intelligence to information processing;
  • Data analysis applied to medicine;
  • Algorithms for medicine;
  • The massive data of medical processing;
  • Medical image processing;
  • e-Health.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Dr. Antonio Sarasa-Cabezuelo
Dr. Francesc Pozo
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • deep learning
  • machine learning
  • artificial intelligence
  • data analysis
  • algorithms
  • big data

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Related Special Issue

Published Papers (3 papers)

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Research

26 pages, 3741 KiB  
Article
Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
by Rym Nassih and Abdelaziz Berrado
Algorithms 2025, 18(3), 136; https://doi.org/10.3390/a18030136 - 3 Mar 2025
Viewed by 402
Abstract
The Patient Rule Induction Method is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low. It falls in the subgroup discovery field, where [...] Read more.
The Patient Rule Induction Method is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low. It falls in the subgroup discovery field, where finding small groups is more relevant for the explainability of the results, although it is not a classification technique, per se. In this paper, we introduce a new framework for breast cancer classification based on the PRIM. This new method involves, first, the random choice of different input spaces for each class label; second, the organization and pruning of the rules using metarules; and finally, it also includes the proposition of a way to handle the class overlapping and, hence, define the final classifier. The framework is tested on five real-life breast cancer datasets compared to three often-used algorithms for breast cancer classification: XG Boost, Logistic Regression, and Random Forest. Across the four metrics and datasets, both our PRIM-based framework and Random Forest demonstrate robust performance, with our framework showing notable accuracy and recall. XGBoost maintains strong F1-scores across the board, indicating balanced precision and recall. On the other hand, Logistic Regression, while competent, generally underperforms compared to the other algorithms, especially in terms of accuracy and recall, achieving 94.1% accuracy against 96.8% and 85.4% recall against 94.2% for the PRIM-based framework on the Wisconsin dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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15 pages, 2843 KiB  
Article
MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
by Botao Liu, Changqi Shi and Ming Zhao
Algorithms 2025, 18(1), 42; https://doi.org/10.3390/a18010042 - 12 Jan 2025
Viewed by 1042
Abstract
The colonoscopy procedure heavily relies on the operator’s expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps [...] Read more.
The colonoscopy procedure heavily relies on the operator’s expertise, underscoring the importance of automated polyp segmentation techniques in enhancing the efficiency and accuracy of colorectal cancer diagnosis. Nevertheless, achieving precise segmentation remains a significant challenge due to the high visual similarity between polyps and their backgrounds, blurred boundaries, and complex localization. To address these challenges, a Multi-scale Selective Edge-Aware Network has been proposed to facilitate polyp segmentation. The model consists of three key components: (1) an Edge Feature Extractor (EFE) that captures polyp edge features with precision during the initial encoding phase, (2) the Cross-layer Context Fusion (CCF) block designed to extract and integrate multi-scale contextual information from diverse receptive fields, and (3) the Selective Edge Aware (SEA) module that enhances sensitivity to high-frequency edge details during the decoding phase, thereby improving edge preservation and segmentation accuracy. The effectiveness of our model has been rigorously validated on the Kvasir-SEG, Kvasir-Sessile, and BKAI datasets, achieving mean Dice scores of 91.92%, 82.10%, and 92.24%, respectively, on the test sets. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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22 pages, 3297 KiB  
Article
Sleep Apnea Classification Using the Mean Euler–Poincaré Characteristic and AI Techniques
by Moises Ramos-Martinez, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, Mario Martínez García, Mayra G. Mena-Enriquez, Estela Sarmiento-Bustos, Juan Carlos Mixteco-Sánchez, Erasmo Misael Rentería-Vargas, Jesús E. Valdez-Resendiz and Jesse Yoe Rumbo-Morales
Algorithms 2024, 17(11), 527; https://doi.org/10.3390/a17110527 - 15 Nov 2024
Viewed by 882
Abstract
Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural [...] Read more.
Sleep apnea is a sleep disorder that disrupts breathing during sleep. This study aims to classify sleep apnea using a machine learning approach and a Euler–Poincaré characteristic (EPC) model derived from electrocardiogram (ECG) signals. An ensemble K-nearest neighbors classifier and a feedforward neural network were implemented using the EPC model as inputs. ECG signals were preprocessed with a polynomial-based scheme to reduce noise, and the processed signals were transformed into a non-Gaussian physiological random field (NGPRF) for EPC model extraction from excursion sets. The classifiers were then applied to the EPC model inputs. Using the Apnea-ECG dataset, the proposed method achieved an accuracy of 98.5%, sensitivity of 94.5%, and specificity of 100%. Combining machine learning methods and geometrical features can effectively diagnose sleep apnea from single-lead ECG signals. The EPC model enhances clinical decision-making for evaluating this disease. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Medicine (2nd Edition))
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