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Machine Learning for Biomedical Data Processing
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Machine Learning for Biomedical Data Processing

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990). This special issue belongs to the section "Data".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 57928

Special Issue Editors

Prof. Dr. Abdulhamit Subasi

E-Mail Website
Guest Editor
Institute of Biomedicine, Faculty of Medicine, University of Turku, 20520 Turku, Finland
Interests: machine learning; pattern recognition; data mining; biomedical signal processing
Prof. Dr. Humaira Nisar

E-Mail Website
Guest Editor
Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
Interests: signal and image processing; EEG signal analysis; neurofeedback
Dr. Saeed Mian Qaisar

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Interests: event-driven systems; signal processing; circuits and systems; machine learning; computational complexity; embedded systems; battery management systems; bioinformatics; healthcare; biomedical; positron emission tomography (PET)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical signal processing involves the treatment and analysis of bio-signal measurements. It is done in order to provide useful information which clinicians can use to make decisions. Novel signal processing methods have assisted in revealing information that entirely altered the previous approaches taken in the diagnosis of different diseases. In order to analyze biomedical signals, biomedical engineers use different types of signal processing and machine learning techniques. By using intelligent biomedical analysis tools, the signals can be analyzed by software to help physicians gain greater insights and to make better decisions in clinical assessments.

Nowadays, there is a great deal of interest in machine learning applications in health, biomedicine, and biomedical engineering. The recent advances in biomedical signal processing and machine learning have brought forth incredible progress to different areas in signal analysis and processing, including biometrics, medical data processing, etc. The application-oriented and data-driven bio-signal analysis and processing applications, not only benefit from the machine learning algorithms, but also encourage the development of intelligent techniques.

The purpose of this Special Issue is to present recent advances in signal processing and machine learning for biomedical signal analysis. We are targeting original research works in this field, covering new theories, algorithms, implementations, and applications for signal and data analytics. Potential topics of interests are related to recent advances in machine learning in signal analysis and processing, but are not limited to them:

  • Biomedical Signal Processing and Analysis
  • Biomedical Image Processing and Analysis
  • Brain Computer Interface
  • Human Machine Interfaces
  • Neural Rehabilitation Engineering
  • Biomedical Data processing for Big Data
  • Information forensics and security
  • The Internet of Things and RFID
  • Machine learning for signal/image processing
  • Signal/Image Processing for Brain Machine Interface
  • Time-frequency and Non-stationary Biosignal Analysis
  • Machine learning for biomedical signal/image processing
  • Machine Learning in Biomedical Applications
  • Biometrics with biomedical signals

Prof. Dr. Abdulhamit Subasi
Prof. Dr. Humaira Nisar
Dr. Saeed Mian Qaisar
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1800 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

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18 pages, 4649 KiB  
Article
PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning
by Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque and Chindo Hicks
Mach. Learn. Knowl. Extr. 2023, 5(4), 1302-1319; https://doi.org/10.3390/make5040066 - 27 Sep 2023
Viewed by 2110
Abstract
A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these [...] Read more.
A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML and stacking algorithms. ML algorithms accurately distinguished indolent from aggressive PCas. With the accuracy of 96%, the stacking model was superior to individual ML algorithms when all samples with primary Gleason Grades 6 to 10 were used. Excluding samples with Gleason Grade 7 improved accuracy to 97%. This study shows that ML algorithms and stacking models are powerful approaches for the accurate classification of indolent versus aggressive PCas. Future implementation of this methodology may significantly impact clinical decision making and patient outcomes in the clinical management of prostate cancer. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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27 pages, 8766 KiB  
Article
Alzheimer’s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier
by Amar Shukla, Rajeev Tiwari and Shamik Tiwari
Mach. Learn. Knowl. Extr. 2023, 5(2), 512-538; https://doi.org/10.3390/make5020031 - 18 May 2023
Cited by 20 | Viewed by 5229
Abstract
Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable [...] Read more.
Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable techniques for the detection of AD and its stages still require a greater extent of research. In this study, a multimodal image-fusion method is initially proposed for the fusion of two different modalities, i.e., PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging). Further, the features obtained from fused and non-fused biomarkers are passed to the ensemble classifier with a Random Forest-based feature selection strategy. Three classes of Alzheimer’s disease are used in this work, namely AD, MCI (Mild Cognitive Impairment) and CN (Cognitive Normal). In the resulting analysis, the Binary classifications, i.e., AD vs. CN and MCI vs. CN, attained an accuracy (Acc) of 99% in both cases. The class AD vs. MCI detection achieved an adequate accuracy (Acc) of 91%. Furthermore, the Multi Class classification, i.e., AD vs. MCI vs. CN, achieved 96% (Acc). Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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25 pages, 12948 KiB  
Article
3t2FTS: A Novel Feature Transform Strategy to Classify 3D MRI Voxels and Its Application on HGG/LGG Classification
by Abdulsalam Hajmohamad and Hasan Koyuncu
Mach. Learn. Knowl. Extr. 2023, 5(2), 359-383; https://doi.org/10.3390/make5020022 - 6 Apr 2023
Cited by 3 | Viewed by 2747
Abstract
The distinction between high-grade glioma (HGG) and low-grade glioma (LGG) is generally performed with two-dimensional (2D) image analyses that constitute semi-automated tumor classification. However, a fully automated computer-aided diagnosis (CAD) can only be realized using an adaptive classification framework based on three-dimensional (3D) [...] Read more.
The distinction between high-grade glioma (HGG) and low-grade glioma (LGG) is generally performed with two-dimensional (2D) image analyses that constitute semi-automated tumor classification. However, a fully automated computer-aided diagnosis (CAD) can only be realized using an adaptive classification framework based on three-dimensional (3D) segmented tumors. In this paper, we handle the classification section of a fully automated CAD related to the aforementioned requirement. For this purpose, a 3D to 2D feature transform strategy (3t2FTS) is presented operating first-order statistics (FOS) in order to form the input data by considering every phase (T1, T2, T1c, and FLAIR) of information on 3D magnetic resonance imaging (3D MRI). Herein, the main aim is the transformation of 3D data analyses into 2D data analyses so as to applicate the information to be fed to the efficient deep learning methods. In other words, 2D identification (2D-ID) of 3D voxels is produced. In our experiments, eight transfer learning models (DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet101, SqueezeNet, VGG19, and Xception) were evaluated to reveal the appropriate one for the output of 3t2FTS and to design the proposed framework categorizing the 210 HGG–75 LGG instances in the BraTS 2017/2018 challenge dataset. The hyperparameters of the models were examined in a comprehensive manner to reveal the highest performance of the models to be reached. In our trails, two-fold cross-validation was considered as the test method to assess system performance. Consequently, the highest performance was observed with the framework including the 3t2FTS and ResNet50 models by achieving 80% classification accuracy for the 3D-based classification of brain tumors. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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17 pages, 1219 KiB  
Article
Using Resistin, Glucose, Age and BMI and Pruning Fuzzy Neural Network for the Construction of Expert Systems in the Prediction of Breast Cancer
by Vinícius Jonathan Silva Araújo, Augusto Junio Guimarães, Paulo Vitor de Campos Souza, Thiago Silva Rezende and Vanessa Souza Araújo
Mach. Learn. Knowl. Extr. 2019, 1(1), 466-482; https://doi.org/10.3390/make1010028 - 14 Feb 2019
Cited by 52 | Viewed by 6368
Abstract
Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such [...] Read more.
Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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Review

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58 pages, 5236 KiB  
Review
A Survey of Deep Learning for Alzheimer’s Disease
by Qinghua Zhou, Jiaji Wang, Xiang Yu, Shuihua Wang and Yudong Zhang
Mach. Learn. Knowl. Extr. 2023, 5(2), 611-668; https://doi.org/10.3390/make5020035 - 9 Jun 2023
Cited by 20 | Viewed by 10186
Abstract
Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related [...] Read more.
Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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Other

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24 pages, 745 KiB  
Systematic Review
Machine Learning and Prediction of Infectious Diseases: A Systematic Review
by Omar Enzo Santangelo, Vito Gentile, Stefano Pizzo, Domiziana Giordano and Fabrizio Cedrone
Mach. Learn. Knowl. Extr. 2023, 5(1), 175-198; https://doi.org/10.3390/make5010013 - 1 Feb 2023
Cited by 55 | Viewed by 28688
Abstract
The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and [...] Read more.
The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results. Full article
(This article belongs to the Special Issue Machine Learning for Biomedical Data Processing)
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