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Beyond Diagnosis: Machine Learning in Prognosis, Prevention, Healthcare, Neurosciences, and...
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Beyond Diagnosis: Machine Learning in Prognosis, Prevention, Healthcare, Neurosciences, and Precision Medicine

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 1 November 2025 | Viewed by 1344

Special Issue Editors

Prof. Dr. Cristian Randieri

E-Mail Website
Guest Editor
Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
Interests: machine learning; advanced machine learning; image processing; classification; pattern recognition; supervised learning management; marketing management; business development; business
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Tradigo

E-Mail Website
Guest Editor
eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
Interests: bioinformatics; computational proteomics and genomics; information extraction from health data
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Pecori

E-Mail Website
Guest Editor
1. SMARTEST Research Centre, Department of Theoretical and Applied Sciences, eCampus University, Via Isimbardi 10, 22060 Novedrate, CO, Italy
2. BioTechTronic Lab, Institute of Materials for Electronics and Magnetism, National Research Council of Italy, Parco Area delle Scienze 37/A, 43124 Parma, PR, Italy
Interests: machine/deep learning; cybersecurity; IoT security; complex systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jakub Mieczkowski

E-Mail Website
Guest Editor
3P-Medicine Laboratory, Medical University of Gdańsk, 80211 Gdańsk, Poland
Interests: computational drug discovery; machine learning applications in molecular modeling; bioinformatics; pharmacology

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the latest advancements and the transformative power of machine learning (ML) in revolutionizing every aspect of healthcare for predicting future health risks (prognosis), developing preventative measures based on individual patient data, and advancing the field of precision medicine. By exploring how ML can identify patients most susceptible to specific diseases and tailor interventions accordingly, this Special Issue aims to shift the focus from reactive healthcare to proactive prevention and personalized treatment strategies. The vast amount of data generated in healthcare settings, from electronic health records to medical images, contains latent information that can be extracted through advanced techniques and holds enormous potential.

This Special Issue focuses on how ML can unlock the power of these data through advanced techniques for their analysis and integration. This will enable clinicians and domain experts to gain deeper insights into patient conditions, predict future health outcomes, and ultimately make more informed decisions to improve patient care. Furthermore, a vital aspect of this exploration is ensuring the interpretability and explainability of ML models. By understanding the reasoning behind the predictions, healthcare professionals can confidently implement preventative measures and personalized treatment plans, ultimately leading to better health outcomes.

Additionally, this Special Issue will delve into the application of ML to psychology, psychological support, remote treatment, and the support of patients and people. The integration of ML in these fields promises to advance the understanding and treatment of mental health conditions, offering personalized and accessible care options. Moreover, the application of ML to neurosciences will be explored, aiming to uncover more profound insights into brain function and develop innovative treatments for neurological disorders.

Research contributions on diagnostic accuracy, treatment efficacy, diagnosis, healthcare delivery, drug discovery, and patient outcomes will be considered through innovative and interpretable ML methodologies or ML-powered simulations.

Contributions in the following areas are welcome:

  • Novel ML algorithms and their application to specific healthcare challenges.
  • Integrating various data sources (electronic health records, medical images, and genomics) with ML for improved diagnostics and prognostics.
  • The application of ML in psychology, psychological support, remote treatment, and the support of patients and people, as well as its use in neurosciences to advance the understanding and treatment of mental health and neurological conditions.
  • The development of explainable and interpretable ML models for building trust and transparency in clinical decision making.
  • Ethical considerations and challenges associated with deploying ML in healthcare settings.
  • Predictive modeling for drug–target interactions.
  • The optimization of molecular simulations to enhance drug candidate selection.
  • AI-driven strategies for improving pharmacokinetic and toxicology predictions.
  • The integration of ML with high-throughput screening for lead optimization.

Short Summary: This Special Issue focuses on the latest advancements in machine learning (ML) and its impact on healthcare, highlighting how ML revolutionizes the ability to predict future health risks, develop preventative measures, and advance precision medicine using vast healthcare data. This issue also covers ML applications in psychology, remote patient support, and neurosciences to enhance mental health treatment, accessibility, and the understanding of brain functions. We particularly welcome contributions on diagnostic accuracy, treatment efficacy, healthcare delivery, drug discovery, and patient outcomes through interpretable ML methodologies.

Prof. Dr. Cristian Randieri
Dr. Giuseppe Tradigo
Dr. Riccardo Pecori
Dr. Jakub Mieczkowski
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. Big Data and Cognitive Computing 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

  • personalized medicine
  • medical imaging
  • predictive modeling in healthcare
  • bioinformatics
  • genomics
  • clinical decision support
  • healthcare data analytics
  • machine learning in drug discovery
  • machine learning simulations
  • interpretable machine learning
  • health informatics
  • patient monitoring
  • ethical issues in medical AI
  • ML in psychology
  • psychological support
  • remote treatment
  • ML in neurosciences

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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14 pages, 1934 KiB  
Article
Evaluating Deep Learning Architectures for Breast Tumor Classification and Ultrasound Image Detection Using Transfer Learning
by Christopher Kormpos, Fotios Zantalis, Stylianos Katsoulis and Grigorios Koulouras
Big Data Cogn. Comput. 2025, 9(5), 111; https://doi.org/10.3390/bdcc9050111 - 23 Apr 2025
Viewed by 529
Abstract
The intersection of medical image classification and deep learning has garnered increasing research interest, particularly in the context of breast tumor detection using ultrasound images. Prior studies have predominantly focused on image classification, segmentation, and feature extraction, often assuming that the input images, [...] Read more.
The intersection of medical image classification and deep learning has garnered increasing research interest, particularly in the context of breast tumor detection using ultrasound images. Prior studies have predominantly focused on image classification, segmentation, and feature extraction, often assuming that the input images, whether sourced from healthcare professionals or individuals, are valid and relevant for analysis. To address this, we propose an initial binary classification filter to distinguish between relevant and irrelevant images, ensuring only meaningful data proceeds to subsequent analysis. However, the primary focus of this study lies in investigating the performance of a hierarchical two-tier classification architecture compared to a traditional flat three-class classification model, by employing a well-established breast ultrasound images dataset. Specifically, we explore whether sequentially breaking down the problem into binary classifications, first identifying normal versus tumorous tissue and then distinguishing benign from malignant tumors, yields better accuracy and robustness than directly classifying all three categories in a single step. Using a range of evaluation metrics, the hierarchical architecture demonstrates notable advantages in certain critical aspects of model performance. The findings of this study provide valuable guidance for selecting the optimal architecture for the final model, facilitating its seamless integration into a web application for deployment. These insights are further anticipated to advance future algorithm development and broaden the potential of the research applicability across diverse fields. Full article
(This article belongs to the Special Issue Beyond Diagnosis: Machine Learning in Prognosis, Prevention, Healthcare, Neurosciences, and Precision Medicine)
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19 pages, 999 KiB  
Article
Development of a Predictive Model for the Biological Activity of Food and Microbial Metabolites Toward Estrogen Receptor Alpha (ERα) Using Machine Learning
by Maksim Kuznetsov, Olga Chernyavskaya, Mikhail Kutuzov, Daria Vilkova, Olga Novichenko, Alla Stolyarova, Dmitry Mashin and Igor Nikitin
Big Data Cogn. Comput. 2025, 9(4), 86; https://doi.org/10.3390/bdcc9040086 - 1 Apr 2025
Viewed by 334
Abstract
The interaction of estrogen receptor alpha (ERα) with various metabolites—both endogenous and exogenous, such as those present in food products, as well as gut microbiota-derived metabolites—plays a critical role in modulating the hormonal balance in the human body. In this study, we evaluated [...] Read more.
The interaction of estrogen receptor alpha (ERα) with various metabolites—both endogenous and exogenous, such as those present in food products, as well as gut microbiota-derived metabolites—plays a critical role in modulating the hormonal balance in the human body. In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. A meticulously curated dataset comprising 75 molecular descriptors derived from compounds with known ERα activity was assembled, enabling the model to achieve an accuracy of 89.4% and an F1 score of 0.93, thereby demonstrating high predictive efficacy. Feature importance analysis revealed that both topological and physicochemical descriptors—most notably FractionCSP3 and AromaticProportion—play pivotal roles in the potential binding to ERα. Subsequently, the model was applied to chemicals commonly encountered in food products, such as indole and various phenolic compounds, indicating that approximately 70% of these substances exhibit activity toward ERα. Moreover, our findings suggest that food processing conditions, including fermentation, thermal treatment, and storage parameters, can significantly influence the formation of these active metabolites. These results underscore the promising potential of integrating predictive modeling into food technology and highlight the need for further experimental validation and model refinement to support innovative strategies for developing healthier and more sustainable food products. Full article
(This article belongs to the Special Issue Beyond Diagnosis: Machine Learning in Prognosis, Prevention, Healthcare, Neurosciences, and Precision Medicine)
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