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Electronics
Special Issues
Advanced Machine Learning and AI in Biomedical Diagnostics and Prognostics
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Advanced Machine Learning and AI in Biomedical Diagnostics and Prognostics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 15 July 2026 | Viewed by 343

Special Issue Editors

Prof. Dr. Fırat Hardalaç

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey
Interests: artificial intelligence; deep learning; biomedical; health applications; defense industry
Special Issues, Collections and Topics in MDPI journals
Dr. Kubilay Ayturan

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Gazi University, 06570 Ankara, Turkey
Interests: artificial intelligence; deep learning; neural network; biomedical; text mining

Special Issue Information

Dear Colleagues, 

This Special Issue invites original research and review articles exploring cutting-edge applications of machine learning and artificial intelligence in biomedical diagnostics and prognostics. Topics of interest include, but are not limited to, the development of novel AI-driven methods for disease prediction and early detection, advanced signal processing techniques for biomedical data, and the application of machine learning in personalized medicine. We encourage submissions that present innovative approaches to data analysis, feature engineering, and model optimization in various biomedical contexts, such as cardiovascular health, metabolic disorders, and oncology. The aim of this Issue is to showcase advancements that enhance diagnostic accuracy, improve prognostic capabilities, and contribute to more effective healthcare solutions.

Prof. Dr. Fırat Hardalaç
Dr. Kubilay Ayturan
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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 2400 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

  • machine learning
  • artificial intelligence
  • biomedical applications
  • diagnostics
  • prognostics
  • healthcare
  • personalized medicine
  • feature engineering
  • deep learning
  • medical imaging

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Published Papers (1 paper)

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Research

19 pages, 2679 KB  
Article
Robustness of AIC-Based AR Order Selection in HRV Analysis
by Emi Yuda, Itaru Kaneko, Daisuke Hirahara and Junichiro Hayano
Electronics 2026, 15(6), 1319; https://doi.org/10.3390/electronics15061319 - 21 Mar 2026
Viewed by 116
Abstract
This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to [...] Read more.
This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to estimate the power spectral density (PSD) of heart rate variability (HRV), and accurate order selection is essential for model stability and reliable spectral estimation. Although the AIC is designed to balance model fit and complexity, it suffers from the problem of arbitrary model selection. This study provides a quantitative robustness analysis of information-criterion-based AR order selection under controlled expansion of the search space. Specifically, we investigated the behavior of the AIC using the PhysioNet database (N = 1257) under conditions where the maximum search order was set to an excessively high value (p = 50), far exceeding the commonly recommended range. Our analysis suggested that the AR model began to capture subtle noise and nonstationary components rather than the intrinsic HRV structure, leading to overfitting and excessive order selection, resulting in false peaks in the PSD and reduced robustness. In conclusion, order decisions based solely on information criteria such as the AIC become unstable when the search range is too large. To ensure robustness, it is recommended to complement the AIC with more stringent criteria such as the Bayesian Information Criterion (BIC) or Final Prediction Error (FPE), in addition to the traditional maximum order restriction. Full article
(This article belongs to the Special Issue Advanced Machine Learning and AI in Biomedical Diagnostics and Prognostics)
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