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Advances in Machine Learning for Healthcare Applications
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Advances in Machine Learning for Healthcare Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9273

Special Issue Editors

Dr. Hugo Peixoto

E-Mail Website
Guest Editor
Knowledge Engineering Group, Algoritmi, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
Interests: machine learning; data mining; eHealth, medical information systems; interoperability and integration in healthcare domain
Special Issues, Collections and Topics in MDPI journals
Dr. Júlio Duarte

E-Mail Website
Guest Editor
Intelligent Data Systems, Algoritmi, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Interests: machine learning; data mining; eHealth, medical information systems; interoperability and integration in healthcare domain

Special Issue Information

Dear Colleagues,

The healthcare sector is currently undergoing a transformation due to the implementation of machine learning (ML) techniques. This advancement has unparalleled prospects for improving patient care, optimizing clinical workflows, and supporting medical research endeavours. The primary objective of this Special Issue, titled "Advances in Machine Learning for Healthcare Applications", is to emphasize the most recent advancements and pioneering implementations of machine learning within the healthcare domain. Its purpose is to facilitate the presentation of state-of-the-art breakthroughs, the exchange of knowledge, and the exploration of the obstacles and possibilities associated with the incorporation of ML and artificial intelligence (AI) into several aspects of healthcare.

The healthcare sector produces a substantial amount of data on a daily basis, encompassing many types such as patient records, imaging data, genomic sequences, and electronic health records. ML algorithms have exceptional proficiency in extracting significant patterns and insights from extensive datasets, facilitating precise diagnostics, tailored treatments, and predictive analytics. This Special Issue will set ground for the ways in which ML is revolutionizing the fields of diagnostics, treatment planning, patient monitoring, and management, as well as its impact on public health and epidemiology.

Furthermore, this Special Issue will focus on the ethical, privacy, and regulatory factors associated with the implementation of ML and AI in the healthcare sector. The discourse surrounding the delicate equilibrium between utilizing data to enhance health outcomes and ensuring the protection of patient privacy and autonomy is of utmost importance, particularly in the current era characterized by the prevalence of big data and artificial intelligence.

The contributions in this Special Issue will encompass original research articles, reviews, and case studies that demonstrate novel machine learning applications in the healthcare sector. These contributions will also explore methodological improvements and offer insightful perspectives on the future direction of this dynamic subject. Issues like data heterogeneity, model interpretability, and interdisciplinary teamwork are all obstacles to applying ML solutions in real-world healthcare settings and should also be considered. The objective of this issue is to cultivate a more profound comprehension of the capabilities and constraints of ML and AI in the healthcare sector, hence stimulating ongoing advancements and cooperation in this critical field.

Dr. Hugo Peixoto
Dr. Júlio Duarte
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. Applied Sciences 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 in healthcare
  • clinical workflow optimization
  • predictive analytics in medicine
  • healthcare data analysis
  • personalized treatment planning
  • medical diagnostic algorithms
  • AI ethics in healthcare
  • patient data privacy
  • interdisciplinary collaboration in health AI
  • healthcare AI regulation and policy

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

Published Papers (4 papers)

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Research

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22 pages, 1685 KiB  
Article
Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction
by Jae sung Shin, Jun Ma, Mao Makara, Nak-Jun Sung, Seong Jun Choi, Sung yeup Kim and Min Hong
Appl. Sci. 2025, 15(6), 3071; https://doi.org/10.3390/app15063071 - 12 Mar 2025
Viewed by 532
Abstract
Hearing loss significantly impacts daily communication, making accurate speech audiometry (SA) assessment essential for diagnosis and treatment. However, SA testing is time-consuming and resource-intensive, limiting its accessibility in clinical practice. This study aimed to develop a multi-class classification model that predicts SA results [...] Read more.
Hearing loss significantly impacts daily communication, making accurate speech audiometry (SA) assessment essential for diagnosis and treatment. However, SA testing is time-consuming and resource-intensive, limiting its accessibility in clinical practice. This study aimed to develop a multi-class classification model that predicts SA results using pure-tone audiometry (PTA) data, enabling a more efficient and automated assessment. To achieve this, we implemented and compared MLP, RNN, gradient boosting, and XGBoost models, evaluating their performance using accuracy, F1 score, log loss, and confusion matrix analysis. Experimental results showed that gradient boosting achieved the highest accuracy, 86.22%, while XGBoost demonstrated a more balanced classification performance. The MLP achieved 85.77% and the RNN achieved 85.41%, exhibiting relatively low accuracy, with the RNN showing limitations due to the low temporal dependency of PTA data. Additionally, all models faced challenges predicting class 2 (borderline hearing levels) due to overlapping data distributions. These findings suggest that machine learning models, particularly gradient boosting and XGBoost, outperform deep learning models in SA prediction. Future research should focus on feature engineering, hyperparameter optimization, and ensemble approaches to enhance performance and validate real-world applicability. The proposed model could contribute to automating SA prediction and improving hearing assessment efficiency and patient care. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Healthcare Applications)
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28 pages, 7207 KiB  
Article
Machine Learning Platform for Disease Diagnosis with Contrast CT Scans
by Jennifer Jin, Mira Kim, Soo Dong Kim and Daniel Jin
Appl. Sci. 2024, 14(17), 7822; https://doi.org/10.3390/app14177822 - 3 Sep 2024
Viewed by 2390
Abstract
Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that improves visualization and evaluation of certain abnormalities in the human body, [...] Read more.
Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that improves visualization and evaluation of certain abnormalities in the human body, as well as to observe temporal changes in lesions and tumors across different time phases. However, developing such medical diagnostic systems presents two significant challenges: high technical complexity and substantial development effort. This paper presents a software platform that effectively addresses these challenges. Specifically, we propose a unified software process that fully automates contrast-enhanced CT (CECT)-specific disease diagnosis, with key tasks performed by leveraging task-specific machine learning models to enhance accuracy. The platform incorporates a suite of specialized machine learning models into the diagnostic process, enabling precise diagnosis of lesions, malignancies, tumors, tumor characteristics, and temporal changes over phases. Moreover, the platform has been designed according to the Open–Closed Principle, allowing it to be applicable to a wide range of CECT-based diagnostic systems. The platform has been implemented in Python using the Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a hepatocellular carcinoma (HCC) diagnosis system has been implemented. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Healthcare Applications)
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48 pages, 5009 KiB  
Article
Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
by Gun-Woo Kim, Chan-Yang Ju, Hyeri Seok and Dong-Ho Lee
Appl. Sci. 2024, 14(7), 2715; https://doi.org/10.3390/app14072715 - 24 Mar 2024
Viewed by 1890
Abstract
During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small [...] Read more.
During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Healthcare Applications)
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Review

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27 pages, 2951 KiB  
Review
How Can Artificial Intelligence Identify Knee Osteoarthritis from Radiographic Images with Satisfactory Accuracy?: A Literature Review for 2018–2024
by Said Touahema, Imane Zaimi, Nabila Zrira and Mohamed Nabil Ngote
Appl. Sci. 2024, 14(14), 6333; https://doi.org/10.3390/app14146333 - 20 Jul 2024
Cited by 2 | Viewed by 3428
Abstract
Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play [...] Read more.
Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren–Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals’ confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Healthcare Applications)
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