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Algorithms
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Explainable Artificial Intelligence for Disease Detection and Secure Monitoring Systems
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Explainable Artificial Intelligence for Disease Detection and Secure Monitoring Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1249

Special Issue Editor

Dr. Qurat-Ul-Ain Mastoi

E-Mail Website
Guest Editor
Computer Science and Creative Technologies, University of the West of England, Bristol 133798, UK
Interests: machine learning; healthcare; IoT; cybersecurity AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Health represents a holistic state of physical, mental, and social well-being, not just the absence of illness. Artificial intelligence (AI) has recently become a transformative tool in healthcare, particularly in disease detection, remote monitoring, and personalised care. AI aids in early disease identification, tracking patient health, and tailoring interventions, proving highly effective in supporting medical diagnoses. However, many AI systems function as "black boxes", offering results without clear explanations. This lack of transparency raises concerns among clinicians, who rely on interpretable evidence for decision making, resulting in scepticism and limiting AI's integration into clinical workflows.

To overcome this, innovative methods are needed to improve the explainability of AI systems in healthcare. Explainable deep learning (DL) techniques can address this by clarifying AI-driven diagnoses, building trust among patients and physicians, and promoting broader adoption in medical practice.

This Special Issue focuses on advancements in explainable AI (XAI) for secure healthcare. Contributions are invited on theoretical developments, novel frameworks, and practical applications of XAI in disease detection, secure remote monitoring, and personalised care, encompassing both conventional and pioneering approaches. The Special Issue focuses on the following topics and more.

  • Disease detection methods based on XAI methodologies;
  • XAI-enabled tumour detection and diagnosis;
  • Novel challenges in current XAI-driven health systems;
  • XAI in cardiovascular and neurological disease diagnosis;
  • XAI-driven models for infectious disease monitoring;
  • Human-centric AI for disease diagnosis;
  • The impact of XAI on clinical decision support systems;
  • Integrating XAI with IoT-based health monitoring devices;
  • XAI-driven early warning systems for chronic conditions;
  • Explainable AI for secure IoT-enabled remote health monitoring;
  • Privacy-preserving explainable models for healthcare monitoring systems;
  • Explainable AI for cybersecurity in connected healthcare systems.

Dr. Qurat-Ul-Ain Mastoi
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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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

  • explainable AI
  • disease detection
  • secure healthcare
  • healthcare monitoring

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Published Papers (1 paper)

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Research

22 pages, 1196 KB  
Article
Interpretable Machine Learning for Coronary Artery Disease Risk Stratification: A SHAP-Based Analysis
by Nurdaulet Tasmurzayev, Zhanel Baigarayeva, Bibars Amangeldy, Baglan Imanbek, Shugyla Kurmanbek, Gulmira Dikhanbayeva and Gulshat Amirkhanova
Algorithms 2025, 18(11), 697; https://doi.org/10.3390/a18110697 - 3 Nov 2025
Viewed by 772
Abstract
Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop [...] Read more.
Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop and rigorously evaluate a calibrated, interpretable machine learning framework for CAD prediction using 56 routinely collected clinical and demographic variables from the Z-Alizadeh Sani dataset (n = 303). A systematic protocol involving comprehensive preprocessing, class rebalancing using SMOTE, and grid-search hyperparameter tuning was applied to five distinct classifiers. The XGBoost model demonstrated the highest predictive performance, achieving an accuracy of 0.9011, an F1 score of 0.8163, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.92. Post hoc interpretability analysis using SHAP (Shapley Additive Explanations) identified HTN, valvular heart disease (VHD), and diabetes mellitus (DM) as the most significant predictors of CAD. Furthermore, calibration analysis confirmed that the mode’s probability estimates are reliable for clinical risk stratification. This work presents a robust framework that combines high predictive accuracy with clinical interpretability, offering a promising tool for early CAD screening and decision support. Full article
(This article belongs to the Special Issue Explainable Artificial Intelligence for Disease Detection and Secure Monitoring Systems)
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