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Inventions
Special Issues
Machine Learning Applications in Healthcare and Disease Prediction
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Machine Learning Applications in Healthcare and Disease Prediction

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Design, Modeling and Computing Methods".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 653

Special Issue Editor

Dr. Chiara Iacovelli

E-Mail Website
Guest Editor
Department of Emergency, Anaesthesiology and Intensive Care Medicine, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
Interests: movement analysis; EEG analysis; robotic rehabilitation; neuroscience; artificial intelligence

Special Issue Information

Dear Colleagues,

Machine Learning (ML) is modifying the healthcare field, offering new solutions for disease diagnosis and prediction. With its ability to analyze large amounts of data, ML can identify hidden patterns that might escape the human eye. Common applications include the early detection of diseases such as cancer, heart disease, diabetes, and neurological disorders, where ML algorithms can analyze medical images, genetic data, and clinical information to detect early warning signs. In addition, ML is being used in outbreak prediction, continuous health monitoring, and personalized treatments, improving the effectiveness of therapies. Technology is also supporting the creation of wearable devices that collect data in real-time, enabling physicians to operate early.

This Special Issue aims to highlight recent advances in the application of machine learning techniques to biomedical data analysis, clinical decision support, early diagnosis, and the prediction and management of diseases.

This Special Issue will serve as a platform for interdisciplinary contributions that bridge computational methodologies and practical healthcare applications, addressing both technical innovations and clinical impacts.

Dr. Chiara Iacovelli
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. Inventions 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 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 (ML)
  • healthcare AI
  • disease prediction
  • early diagnosis
  • personalized medicine
  • health data analysis
  • wearable health devices

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

Published Papers (2 papers)

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Research

11 pages, 596 KiB  
Article
Prediction of Major Adverse Cardiovascular Events in Atrial Fibrillation: A Comparison Between Machine Learning Techniques and CHA2DS2-VASc Score
by Pedro Moltó-Balado, Josep-Lluis Clua-Espuny, Silvia Reverté-Villarroya, Victor Alonso-Barberán, Maria Teresa Balado-Albiol, Andrea Simeó-Monzó, Jorge Canela-Royo and Alba del Barrio-González
Inventions 2025, 10(4), 60; https://doi.org/10.3390/inventions10040060 - 22 Jul 2025
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Abstract
Background/Objectives: Atrial fibrillation (AF) is a prevalent arrhythmia associated with a high risk of major adverse cardiovascular events (MACEs). This study aimed to compare the predictive ability of an ML model and the CHA2DS2-VASc score in predicting MACEs in [...] Read more.
Background/Objectives: Atrial fibrillation (AF) is a prevalent arrhythmia associated with a high risk of major adverse cardiovascular events (MACEs). This study aimed to compare the predictive ability of an ML model and the CHA2DS2-VASc score in predicting MACEs in AF patients using machine learning (ML) techniques. Methods: A cohort of 40,297 individuals aged 65–95 from the Terres de l’Ebre region (Catalonia, Spain) and diagnosed with AF between 2015 and 2016 was analyzed. ML algorithms, particularly AdaBoost, were used to predict MACEs, and the performance of the models was evaluated through metrics such as recall, area under the ROC curve (AUC), and accuracy. Results: The AdaBoost model outperformed CHA2DS2-VASc, achieving an accuracy of 99.99%, precision of 0.9994, recall of 1, and an AUC of 99.99%, compared to CHA2DS2-VASc’s AUC of 81.71%. A statistically significant difference was found using DeLong’s test (p = 0.0034) between the models, indicating the superior performance of the AdaBoost model in predicting MACEs. Conclusions: The AdaBoost model provides significantly better prediction of MACE in AF patients than the CHA2DS2-VASc score, demonstrating the potential of ML algorithms for personalized risk assessment and early detection in clinical settings. Further validation and computational resources are necessary to enable broader implementation. Full article
(This article belongs to the Special Issue Machine Learning Applications in Healthcare and Disease Prediction)
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11 pages, 1132 KiB  
Article
Custom-Tailored Radiology Research via Retrieval-Augmented Generation: A Secure Institutionally Deployed Large Language Model System
by Michael Welsh, Julian Lopez-Rippe, Dana Alkhulaifat, Vahid Khalkhali, Xinmeng Wang, Mario Sinti-Ycochea and Susan Sotardi
Inventions 2025, 10(4), 55; https://doi.org/10.3390/inventions10040055 - 8 Jul 2025
Viewed by 345
Abstract
Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the [...] Read more.
Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the inability to ensure patient data protection. In this work, we present a secure, custom-designed retrieval-augmented generation (RAG) LLM system deployed entirely within our institution and tailored for radiology research. Radiology researchers at our institution evaluated the system against GPT-4-Consensus through a blinded survey assessing factual accuracy (FA), citation relevance (CR), and perceived performance (PP) using 5-point Likert scales. Our system achieved mean ± SD scores of 4.15 ± 0.99 for FA, 3.70 ± 1.17 for CR, and 3.55 ± 1.39 for PP. In comparison, GPT-4-Consensus obtained 4.25 ± 0.72, 3.85 ± 1.23, and 3.90 ± 1.12 for the same metrics, respectively. No statistically significant differences were observed (p = 0.97, 0.65, 0.42), and 50% of participants preferred our system’s output. These results validate that secure, local RAG-based LLMs can match state-of-the-art performance while preserving privacy and adaptability, offering a scalable tool for medical research environments. Full article
(This article belongs to the Special Issue Machine Learning Applications in Healthcare and Disease Prediction)
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