{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T08:36:15Z","timestamp":1761208575266,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Pandemics place extraordinary pressure on healthcare systems, particularly in identifying and prioritizing high-risk groups such as the elderly, pregnant women, and individuals with chronic diseases. Existing Artificial Intelligence models often fall short, focusing on single diseases, lacking interpretability, and overlooking patient-specific vulnerabilities. To address these gaps, we propose an Explainable Deep Learning approach for identifying at-risk patients during pandemics (AI-RiskX). AI-RiskX performs risk classification of patients diagnosed with COVID-19 or related infections to support timely intervention and resource allocation. Unlike previous models, AI-RiskX integrates five public datasets (asthma, diabetes, heart, kidney, and thyroid), employs the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing, and uses a hybrid convolutional neural network\u2013long short-term memory model (CNN\u2013LSTM) for robust disease classification. SHAP (SHapley Additive exPlanations) enables both individual and population-level interpretability, while a post-prediction rule-based module stratifies patients by age and pregnancy status. Achieving 98.78% accuracy, AI-RiskX offers a scalable, interpretable solution for equitable classification and decision support in public health emergencies.<\/jats:p>","DOI":"10.3390\/bioengineering12101127","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:17:35Z","timestamp":1761056255000},"page":"1127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-RiskX: An Explainable Deep Learning Approach for Identifying At-Risk Patients During Pandemics"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6055-0854","authenticated-orcid":false,"given":"Nada","family":"Zendaoui","sequence":"first","affiliation":[{"name":"Institute of Mathematics and Computer Science, Abdelhafid Boussouf University Center of Mila, Mila 43000, Algeria"},{"name":"LIRE Laboratory of Constantine 2, Constantine 25000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6105-2152","authenticated-orcid":false,"given":"Nardjes","family":"Bouchemal","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, Abdelhafid Boussouf University Center of Mila, Mila 43000, Algeria"},{"name":"LISI Laboratory of Intelligent Systems and Informatics, Mila 43000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohamed Rafik Aymene","family":"Berkani","sequence":"additional","affiliation":[{"name":"LSEA Research Laboratory in Advanced Electronics Systems, University Yahia Fares of Medea, Medea 26000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4950-191X","authenticated-orcid":false,"given":"Mounira","family":"Bouzahzah","sequence":"additional","affiliation":[{"name":"Institute of Mathematics and Computer Science, Abdelhafid Boussouf University Center of Mila, Mila 43000, Algeria"},{"name":"LISI Laboratory of Intelligent Systems and Informatics, Mila 43000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6524-7352","authenticated-orcid":false,"given":"Saad","family":"Harous","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8294-9276","authenticated-orcid":false,"given":"Naila","family":"Bouchemal","sequence":"additional","affiliation":[{"name":"LyRIDS ECE Ecole d\u2019Ing\u00e9nieurs de Paris, 75015 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dorjee, K., Kim, H., Bonomo, E., and Dolma, R. (2020). Prevalence and predictors of death and severe disease in patients hospitalized due to COVID-19: A comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0243191"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.jaci.2020.07.018","article-title":"Risk factors for hospitalization, intensive care, and mortality among patients with asthma and COVID-19","volume":"146","author":"Wang","year":"2020","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Berumen-Lechuga, M.G., Lea\u00f1os-Miranda, A., Molina-P\u00e9rez, C.J., Garc\u00eda-Cortes, L.R., and Palomo-Pi\u00f1\u00f3n, S. (2023). Risk Factors for Severe\u2013Critical COVID-19 in Pregnant Women. J. Clin. Med., 12.","DOI":"10.3390\/jcm12185812"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/S0140-6736(20)30566-3","article-title":"Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study","volume":"395","author":"Zhou","year":"2020","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"m1966","DOI":"10.1136\/bmj.m1966","article-title":"Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: Prospective cohort study","volume":"369","author":"Petrilli","year":"2020","journal-title":"BMJ"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","article-title":"The false hope of current approaches to explainable artificial intelligence in health care","volume":"3","author":"Ghassemi","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e23629","DOI":"10.1016\/j.heliyon.2023.e23629","article-title":"Risk factors for severe disease and mortality in children with COVID-19","volume":"10","year":"2024","journal-title":"Heliyon"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.2147\/RMHP.S456155","article-title":"The severity of the COVID-19 among chronic disease patients in the selected hospitals in Riyadh, Saudi Arabia\u2014A retrospective study","volume":"17","author":"Khan","year":"2024","journal-title":"Risk Manag. Healthc. Policy"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rebiai, M., Berkani, M.R.A., Besseri, I., Tobbal, S., Bengherbia, B., and Zendaoui, N. (2024, January 14\u201315). Enhanced Cardiac Disease Detection Using MFCC Feature Extraction from PCG Signals. Proceedings of the 2024 International Conference on Telecommunications and Intelligent Systems (ICTIS), Djelfa, Algeria.","DOI":"10.1109\/ICTIS62692.2024.10894610"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Berkani, M.R.A., Chouchane, A., Himeur, Y., Ouamane, A., and Amira, A. (2023, January 8\u20139). An Intelligent Edge-Deployable Indoor Air Quality Monitoring and Activity Recognition Approach. Proceedings of the 2023 6th International Conference on Signal Processing and Information Security (ICSPIS), Dubai, United Arab Emirates.","DOI":"10.1109\/ICSPIS60075.2023.10343626"},{"key":"ref_13","unstructured":"World Health Organization (2024). COVID-19 and Noncommunicable Diseases: Evidence and Actions for Better Outcomes (Corrigendum 7\u2013Final Version), World Health Organization. Available online: https:\/\/www.who.int\/docs\/default-source\/inaugural-who-partners-forum\/covid-19-and-ncds---final---corr7.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Booth, A., Reed, A.B., Ponzo, S., Yassaee, A., Aral, M., Plans, D., Labrique, A., and Mohan, D. (2021). Population risk factors for severe disease and mortality in COVID-19: A global systematic review and meta-analysis. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247461"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100210","DOI":"10.1016\/j.simpa.2021.100210","article-title":"COV-ADSX: An automated detection system using X-ray images, deep learning, and XGBoost for COVID-19","volume":"11","author":"Hasani","year":"2020","journal-title":"Softw. Impacts"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ezzoddin, M., Nasiri, H., and Dorrigiv, M. (2022, January 25\u201327). Diagnosis of COVID-19 cases from chest X-ray images using deep neural network and LightGBM. Proceedings of the 2022 International Conference on Machine Vision and Image Processing (MVIP), Hong Kong, China.","DOI":"10.1109\/MVIP53647.2022.9738760"},{"key":"ref_17","first-page":"6688934","article-title":"A Proposed Technique Using Machine Learning for the Prediction of Diabetes Disease through a Mobile App","volume":"2024","author":"Karam","year":"2024","journal-title":"Int. J. Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Guleria, P., Naga Srinivasu, P., Ahmed, S., Almusallam, N., and Alarfaj, F.K. (2022). XAI framework for cardiovascular disease prediction using classification techniques. Electronics, 11.","DOI":"10.3390\/electronics11244086"},{"key":"ref_19","first-page":"1","article-title":"Early detection of coronary heart disease based on machine learning methods","volume":"4","year":"2022","journal-title":"Med. Rec."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"199","DOI":"10.32604\/iasc.2023.025819","article-title":"A novel technique for detecting various thyroid diseases using deep learning","volume":"35","author":"Prathibha","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Lee, V.C.S., Rong, J., Lee, J.C., and Liu, F. (2022). Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography. Comput. Methods Programs Biomed., 220.","DOI":"10.1016\/j.cmpb.2022.106823"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"58403","DOI":"10.1109\/ACCESS.2021.3073086","article-title":"An early detection of asthma using BOMLA detector","volume":"9","author":"Awal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_23","first-page":"140","article-title":"Intelligent diagnosis of Asthma using machine learning algorithms","volume":"5","author":"Tahasamadsoltaniheris","year":"2013","journal-title":"Int. Res. J. Appl. Basic Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bottrighi, A., Pennisi, M., Roveta, A., Massarino, C., Cassinari, A., Betti, M., Bolgeo, T., Bertolotti, M., Rava, E., and Maconi, A. (2022). A machine learning approach for predicting high risk hospitalized patients with COVID-19 SARS-Cov-2. BMC Med. Inform. Decis. Mak., 22.","DOI":"10.1186\/s12911-022-02076-1"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Quiroz-Ju\u00e1rez, M.A., Torres-G\u00f3mez, A., Hoyo-Ulloa, I., Le\u00f3n-Montiel, R.J., and U\u2019Ren, A.B. (2021). Identification of high-risk COVID-19 patients using machine learning. PLoS ONE, 16.","DOI":"10.1101\/2021.02.10.21251510"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37370","DOI":"10.1109\/ACCESS.2024.3422319","article-title":"A comparative analysis of LIME and SHAP interpreters with explainable ML-based diabetes predictions","volume":"13","author":"Ahmed","year":"2024","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104370","DOI":"10.1016\/j.rineng.2025.104370","article-title":"HXAI-ML: A Hybrid Explainable Artificial Intelligence Based Machine Learning Model for Cardiovascular Heart Disease Detection","volume":"25","author":"Talukder","year":"2025","journal-title":"Results Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bilal, A., Alzahrani, A., Almohammadi, K., Saleem, M., Farooq, M.S., and Sarwar, R. (2025). Explainable AI-driven intelligent system for precision forecasting in cardiovascular disease. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1596335"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mahmood, S., Hasan, R., Hussain, S., and Adhikari, R. (2025). An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques. World, 6.","DOI":"10.3390\/world6010015"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lyu, Y., Jiang, Q.-C., Yuan, S., Hong, J., Chen, C.-F., Wu, H.-M., Wang, Y.-Q., Shi, Y.-J., Yan, H.-X., and Xu, J. (2025). Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-14645-1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e36112","DOI":"10.1016\/j.heliyon.2024.e36112","article-title":"An explainable Artificial Intelligence software system for predicting diabetes","volume":"10","author":"Srinivasu","year":"2024","journal-title":"Heliyon"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chadaga, K., Prabhu, S., Sampathila, N., Adiga, V., and Nayak, V. (2024). Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-52428-2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ozawa, T., Chubachi, S., Namkoong, H., Takahashi, T., and Betsuyaku, T. (2025). Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-85733-5"},{"key":"ref_34","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Duckworth, C., Chmiel, F.P., Burns, D.K., Zlatev, Z.D., White, N.M., Daniels, T.W.V., Kiuber, M., and Boniface, M.J. (2021). Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-02481-y"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, Y., He, B., Xu, Z., Zhang, Y., Zhao, X., Huang, Z.-A., Yang, F., Wang, L., Duan, L., and Song, J. (2023). Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire. Briefings Bioinform., 24.","DOI":"10.1093\/bib\/bbac555"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/htl2.12039","article-title":"Diabetes prediction using machine learning and explainable AI techniques","volume":"10","author":"Tasin","year":"2023","journal-title":"Healthc. Technol. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zendaoui, N., Bouchemal, N., and Benabdelhafid, M. (2023, January 2\u20134). AI-LMS: AI-Based Long-Term Monitoring System for Patients in Pandemics: COVID-19 Case Study. Proceedings of the International Conference on Model and Data Engineering, Sousse, Tunisia.","DOI":"10.1007\/978-3-031-49333-1_20"},{"key":"ref_39","unstructured":"Janosi, A., Steinbrunn, W., Pfisterer, M., and Detrano, R. (2025, April 25). Heart Disease. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/45\/heart+disease."},{"key":"ref_40","unstructured":"Selva Balaji (2025, April 25). Diabetes. Mendeley Data, V1, Available online: https:\/\/data.mendeley.com\/datasets\/pd64hfttwy\/1."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1016\/j.procs.2021.08.106","article-title":"Thyroid disease treatment prediction with machine learning approaches","volume":"192","author":"Aversano","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_42","unstructured":"Rabie El Kharoua (2025, April 25). Asthma Disease Dataset. Kaggle. Available online: https:\/\/www.kaggle.com\/dsv\/8669080."},{"key":"ref_43","unstructured":"Rubini, L., Soundarapandian, P., and Eswaran, P. (2025, April 25). Chronic Kidney Disease. UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu\/dataset\/336\/chronic+kidney+disease."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D convolutional neural networks and applications: A survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_47","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA."},{"key":"ref_48","unstructured":"Lipton, Z.C., Kale, D.C., Elkan, C., and Wetzell, R. (2016). Learning to diagnose with LSTM recurrent neural networks. arXiv."},{"key":"ref_49","unstructured":"Alabi, O., Teikari, O., Szwajda, A., Viiki, M., R\u00f6nk\u00f6, E., Pahikkala, T., and Aittokallio, T. (2023). Machine learning explainability for cancer prognosis: A systematic review of techniques, applications, and challenges. Briefings Bioinform., 24."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2400304","DOI":"10.1002\/aisy.202400304","article-title":"A perspective on explainable artificial intelligence methods: SHAP and LIME","volume":"7","author":"Salih","year":"2025","journal-title":"Adv. Intell. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Panda, M., and Mahanta, S.R. (2024). Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP. Explainable, Interpretable, and Transparent AI Systems, CRC Press.","DOI":"10.1201\/9781003442509-6"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bouckaert, R.R., and Frank, E. (2004). Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-540-24775-3_3"},{"key":"ref_54","first-page":"1","article-title":"Statistical Comparisons of Classifiers over Multiple Data Sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Japkowicz, N., and Shah, M. (2011). Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press.","DOI":"10.1017\/CBO9780511921803"},{"key":"ref_56","unstructured":"Abid, A., Abdalla, A., Wolf, T., and Zou, J. (2019, January 9\u201315). Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild. Proceedings of the 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, CA, USA."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T08:32:42Z","timestamp":1761208362000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/10\/1127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":56,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["bioengineering12101127"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12101127","relation":{},"ISSN":["2306-5354"],"issn-type":[{"type":"electronic","value":"2306-5354"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}