Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.6
Journals
Computers
Special Issues
Applications of Machine Learning and Artificial Intelligence for Healthcare
share announcement

Applications of Machine Learning and Artificial Intelligence for Healthcare

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 11181

Special Issue Editor

Dr. Elias Dritsas

E-Mail Website
Guest Editor
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece
Interests: artificial intelligence; big data; data analysis; databases; data mining; data structures; machine learning; privacy; security; trust
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Substantial advances have been made in artificial intelligence (AI) and machine learning (ML) in recent years, and these advancements have great potential for healthcare applications.

The domain of eHealth is emerging alongside the advancement of information and telecommunication technologies and the need for improved healthcare services. At the same time, healthcare applications also face many challenges, such as the difficulties associated with obtaining various types of health-related information, lack of large-sized training data, or even privacy concerns. More specialized research efforts and developments are still needed to address these issues.

This Special Issue aims to provide original, high-quality, innovative ideas and research solutions (for both theoretical and practical challenges) for data analysis and modelling with the aid of artificial intelligence and machine learning in the domain of healthcare.

The key topics of interest include (but are not limited to):

  1. Artificial intelligence in healthcare;
  2. Machine learning in healthcare;
  3. Statistics;
  4. Predictive modeling;
  5. Monitoring;
  6. Data analytics;
  7. Personal health advisor;
  8. Early diagnosis.

Dr. Elias Dritsas
Guest Editor

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. Computers 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 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.

Keywords

  • machine learning
  • artificial intelligence
  • healthcare data
  • e-Health
  • prediction

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 657 KiB  
Article
Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification
by Hai-Long Nguyen, Van Su Pham and Hai-Chau Le
Computers 2024, 13(12), 316; https://doi.org/10.3390/computers13120316 - 27 Nov 2024
Viewed by 326
Abstract
The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The [...] Read more.
The increasing prevalence of heart diseases has driven the development of automated arrhythmia classification systems using machine learning and electrocardiograms (ECGs). This paper presents a novel ensemble learning method for classifying multiple arrhythmia types using 12-lead ECG signals through a blending technique. The framework employs a predetermined meta-model from foundation models, while the remaining models serve as potential base estimators, ranked by accuracy. Using sequential forward selection and meta-feature augmentation, the system determines an optimal base estimator set and creates a meta-dataset for the meta-model, which is optimized through grid search with k-fold cross-validation. Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

12 pages, 8479 KiB  
Article
Automated Generation of Lung Cytological Images from Image Findings Using Text-to-Image Technology
by Atsushi Teramoto, Yuka Kiriyama, Ayano Michiba, Natsuki Yazawa, Tetsuya Tsukamoto, Kazuyoshi Imaizumi and Hiroshi Fujita
Computers 2024, 13(11), 303; https://doi.org/10.3390/computers13110303 - 19 Nov 2024
Viewed by 454
Abstract
Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of [...] Read more.
Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

14 pages, 2039 KiB  
Article
Deep Learning Based Breast Cancer Detection Using Decision Fusion
by Doğu Manalı, Hasan Demirel and Alaa Eleyan
Computers 2024, 13(11), 294; https://doi.org/10.3390/computers13110294 - 14 Nov 2024
Viewed by 679
Abstract
Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer [...] Read more.
Breast cancer, which has the highest mortality and morbidity rates among diseases affecting women, poses a significant threat to their lives and health. Early diagnosis is crucial for effective treatment. Recent advancements in artificial intelligence have enabled innovative techniques for early breast cancer detection. Convolutional neural networks (CNNs) and support vector machines (SVMs) have been used in computer-aided diagnosis (CAD) systems to identify breast tumors from mammograms. However, existing methods often face challenges in accuracy and reliability across diverse diagnostic scenarios. This paper proposes a three parallel channel artificial intelligence-based system. First, SVM distinguishes between different tumor types using local binary pattern (LBP) features. Second, a pre-trained CNN extracts features, and SVM identifies potential tumors. Third, a newly developed CNN is trained and used to classify mammogram images. Finally, a decision fusion that combines results from the three channels to enhance system performance is implemented using different rules. The proposed decision fusion-based system outperforms state-of-the-art alternatives with an overall accuracy of 99.1% using the product rule. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

24 pages, 1353 KiB  
Article
Application of Deep Learning for Heart Attack Prediction with Explainable Artificial Intelligence
by Elias Dritsas and Maria Trigka
Computers 2024, 13(10), 244; https://doi.org/10.3390/computers13100244 - 25 Sep 2024
Viewed by 1841
Abstract
Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive [...] Read more.
Heart disease remains a leading cause of mortality worldwide, and the timely and accurate prediction of heart attack is crucial yet challenging due to the complexity of the condition and the limitations of traditional diagnostic methods. These challenges include the need for resource-intensive diagnostics and the difficulty in interpreting complex predictive models in clinical settings. In this study, we apply and compare the performance of five well-known Deep Learning (DL) models, namely Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a Hybrid model, to a heart attack prediction dataset. Each model was properly tuned and evaluated using accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC) as performance metrics. Additionally, by integrating an Explainable Artificial intelligence (XAI) technique, specifically Shapley Additive Explanations (SHAP), we enhance the interpretability of the predictions, making them actionable for healthcare professionals and thereby enhancing clinical applicability. The experimental results revealed that the Hybrid model prevailed, achieving the highest performance across all metrics. Specifically, the Hybrid model attained an accuracy of 91%, precision of 89%, recall of 90%, F1-score of 89%, and an AUC of 0.95. These results highlighted the Hybrid model’s superior ability to predict heart attacks, attributed to its efficient handling of sequential data and long-term dependencies. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

13 pages, 3622 KiB  
Article
Assessing the Impact of Prolonged Sitting and Poor Posture on Lower Back Pain: A Photogrammetric and Machine Learning Approach
by Valentina Markova, Miroslav Markov, Zornica Petrova and Silviya Filkova
Computers 2024, 13(9), 231; https://doi.org/10.3390/computers13090231 - 14 Sep 2024
Viewed by 5191
Abstract
Prolonged static sitting at the workplace is considered one of the main risks for the development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar [...] Read more.
Prolonged static sitting at the workplace is considered one of the main risks for the development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar discomfort and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. The current study focused on evaluating the relationship between LBP and prolonged sitting in poor posture using photogrammetric images, postural angle calculation, machine learning models, and questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed to recognize poor body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

21 pages, 3742 KiB  
Article
A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach
by Obaid Alotaibi, Sarath Tomy and Eric Pardede
Computers 2024, 13(7), 175; https://doi.org/10.3390/computers13070175 - 16 Jul 2024
Viewed by 1203
Abstract
Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, [...] Read more.
Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
►▼ Show Figures

Figure 1

Review

Jump to: Research

23 pages, 323 KiB  
Review
Advancements in Parkinson’s Disease Diagnosis: A Comprehensive Survey on Biomarker Integration and Machine Learning
by Ruchira Pratihar and Ravi Sankar
Computers 2024, 13(11), 293; https://doi.org/10.3390/computers13110293 - 14 Nov 2024
Viewed by 882
Abstract
This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. It addresses the challenges in the assessment of PD for accurate diagnosis, treatment decisions, and patient care due to difficulties in early and [...] Read more.
This comprehensive review explores the advancements in machine learning algorithms in the diagnosis of Parkinson’s disease (PD) utilizing different biomarkers. It addresses the challenges in the assessment of PD for accurate diagnosis, treatment decisions, and patient care due to difficulties in early and differential diagnosis, subjective clinical assessments, symptom variability, limited objective biomarkers, comorbidity impacts, uneven access to specialized care, and gaps in clinical research. This review provides a detailed review of ongoing biomarker research, technological advancements for objective assessment, and enhanced healthcare infrastructure. It presents a comprehensive evaluation of the use of diverse biomarkers for diagnosing Parkinson’s disease (PD) across various datasets, utilizing machine learning models. Recent research findings are summarized in tables, showcasing key methodologies such as data preprocessing, feature selection, and classification techniques. This review also explores the performance, benefits, and limitations of different diagnostic approaches, providing valuable insights into their effectiveness in PD diagnosis. Moreover, the review addresses the integration of multimodal biomarkers, combining data from different sources to enhance diagnostic accuracy, and disease monitoring. Challenges such as data heterogeneity, variability in symptom progression, and model generalizability are discussed alongside emerging trends and future directions in the field. Ultimately, the application of machine learning (ML) in leveraging diverse biomarkers offers promising avenues for advancing PD diagnosis, paving the way for personalized treatment strategies and improving patient outcomes. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Artificial Intelligence for Healthcare)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop