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Exploring Early Learning Challenges in Children Utilizing Statistical and Explainable Machine Learning

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Developing an interpretable machine learning model for diagnosing gout using clinical and ultrasound features

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Feature Importance Measures as Explanation for Classification Applied to Hospital Readmission Prediction

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Deep survival analysis for interpretable time-varying prediction of preeclampsia risk

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Enabling Real-Time Prediction of In-game Deaths through Telemetry in Counter-Strike: Global Offensive

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Explainable Vertical Federated Learning for Healthcare: Ensuring Privacy and Optimal Accuracy

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Machine learning explainability for survival outcome in head and neck squamous cell carcinoma

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A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction

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Extending Logic Explained Networks to Text Classification

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Bladder cancer gene expression prediction with explainable algorithms

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Explainable AI for Intrusion Prevention: A Review of Techniques and Applications

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Increasing Transparency in Machine Learning through Bootstrap Simulation and Shapely Additive ExplanationsPreprint

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Explainable Machine Learning Method for Open Fault Detection of NPC Inverter Using SHAP and LIME

Al-Kaf, H. A. G., & Lee, K. (2023) · IEEE Conference on Energy Conversion · doi:10.1109/cencon58932.2023.10368888 · Cited by 7

Can integration of Alzheimer’s plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?

Gebre, R. K., Graff‐Radford, J., Ramanan, V. K., Raghavan, S., Hofrenning, E. I., Przybelski, S. A., Nguyen, A. T., Lesnick, T. G., Gunter, J. L., Algeciras‐Schimnich, A., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae300 · Cited by 8

A Hybrid Approach to Credit Risk Assessment Using Bill Payment Habits Data and Explainable Artificial Intelligence

Bulut, C., & Arslan, E. (2025) · Applied Sciences · doi:10.3390/app15105723 · Cited by 7

Improving Network Classification Accuracy through Feature Clustering and Ensemble Machine Learning with Explainable AI

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Explainable Chronic Kidney Disease (CKD) Prediction using Deep Learning and Shapley Additive Explanations (SHAP)

Jhumka, K., Auzine, M. M., Khan, M. H.-M., Casseem, M. S., Fedally, S. A., & Mungloo-Dilmohamud, Z. (2023) · International Conference on Advances in Artificial Intelligence (ICAAI) · doi:10.1145/3633598.3633604 · Cited by 6

Enhancing stability and interpretability in the study of strength behavior for coarse-grained soils

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Improved Yield Prediction and Failure Analysis in Semiconductor Manufacturing with XGBoost and Shapley Additive exPlanations Models

Wang, S., & Chen, Y. (2024) · IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) · doi:10.1109/ipfa61654.2024.10691196 · Cited by 7

Using Explainable Machine Learning Methods to Predict the Survivability Rate of Pediatric Respiratory Diseases

Kumar, R., Srirama, V., Chadaga, K., Muralikrishna, H., Sampathila, N., Prabhu, S., & Chadaga, R. (2024) · IEEE Access · doi:10.1109/access.2024.3516045 · Cited by 7

NATE: Non-pArameTric approach for Explainable credit scoring on imbalanced class

Han, S., & Jung, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0316454 · Cited by 6

Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database

Li, F., Wang, Z., Bian, R., Xue, Z., Cai, J., Zhou, Y., & Wang, Z. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-087427 · Cited by 6

Enhancing Credit Risk Prediction through an Ensemble of Explainable Model

Sun, X., Liu, J., & Zhang, Y. T. (2025) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-025-5663-y · Cited by 10

What factors enhance students’ achievement? A machine learning and interpretable methods approach

Mao, H., Khanal, R., Qu, C., Kong, H., & Jiang, T. (2025) · PLoS ONE · doi:10.1371/journal.pone.0323345 · Cited by 8

Credit Card Fraud Detection Through Explainable Artificial Intelligence for Managerial Oversight

Muksalmina, M., Syahyana, A., Hidayatullah, F., Idroes, G. M., & Noviandy, T. R. (2025) · Indatu Journal of Management and Accounting · doi:10.60084/ijma.v3i1.301 · Cited by 6

An Application of Explainable Artificial Intelligence in Credit Scoring

Nguyen, S. P., & Truong, N. Q. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-43601-7_21 · Cited by 5

Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton

Iatrou, M., Tziachris, P., Bilias, F., Kekelis, P., Pavlakis, C., Theofilidou, A., Papadopoulos, I., Strouthopoulos, G., Giannopoulos, G., Arampatzis, D. A., et al. (2025) · Nitrogen · doi:10.3390/nitrogen6020029 · Cited by 5

Identifying suicidal ideations from social media posts using deep learning and explainable AI-driven approach

Zulfiker, M. S., Kabir, N., Biswas, A. A., Adan, M. M. I. Y., & Uddin, M. S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113813 · Cited by 5

Interpretable Heart Failure Identification Utilizing Auto Machine Learning Tools

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Early autism detection: a review of emerging technologies, biomarkers, and explainable AI approaches

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A comprehensive literature review on ai-based credit assessment in digital lending: exploring models, transparency, alternative data, and real-time learning

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Integration of Explainable Artificial Intelligence (XAI) in the Development of Disease Prediction and Medicine Recommendation System

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RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines

Shyalika, C., Roy, K., Prasad, R., Kalach, F. E., Zi, Y., Mittal, P., Narayanan, V., Harik, R., & Sheth, A. (2024) · Sensors · doi:10.3390/s24103244 · Cited by 5

Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks

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A Two‐Stage Interpretable Model to Explain Classifier in Credit Risk Prediction

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An interpretable machine learning model for predicting p-y curves of monopiles in clay

Li, B., Qi, W., Wang, Y., Gao, F., Jing, L., Wang, S. Y., & He, B. G. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.123245 · Cited by 4

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Antar, M., Hassan, R., & Barka, D. (2025) · Journal of Corporate Accounting & Finance · doi:10.1002/jcaf.70020 · Cited by 4

KDBI special issue: Explainability feature selection framework application for LSTM multivariate time‐series forecast self optimization

Rodrigues, E. M., Baghoussi, Y., & Mendes‐Moreira, J. (2024) · Expert Systems · doi:10.1111/exsy.13674 · Cited by 4

Boosting Credit Risk Data Quality Using Machine Learning and eXplainable AI Techniques

Tiukhova, E., Salcuni, A., Oguz, C., Niglio, M., Storti, G., Forte, F., Baesens, B., & Snoeck, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74643-7_30 · Cited by 4

Early Warning of Companies' Credit Risk Based on Machine Learning

Tan, B., & Lin, Y. (2023) · International Journal of Information Technologies and Systems Approach · doi:10.4018/ijitsa.324067 · Cited by 4

Can ESG Shape Cost of Capital? A Bibliometric Review and Empirical Analysis Through MLPreprint

Bussmann, N., Tanda, A., & Yu, E. P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4173890 · Cited by 4

Developing Explainable Artificial Intelligence Models for Space Science Applications

Agarwal, A. K., Bhardwaj, R., Tiwary, G., Aljohani, A. A., Kawatra, R., & Das, A. (2025) · Space Science & Technology · doi:10.34133/space.0255 · Cited by 3

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Louhichi, M., Nesmaoui, R., & Lazaar, M. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160770 · Cited by 3

An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency

Aydoğan, B., & Aytekin, T. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02541-z · Cited by 5

Fire susceptibility assessment in the Carpathians using an interpretable framework

Manczinger, M., Kovács, L., & Kovács, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-10296-4 · Cited by 3

Risk stratification in diabetic kidney disease: a review of prediction models for methodological advances and clinical application

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Dixit, M., Kansal, I., Khullar, V., Kumar, R., & Kumar, S. (2024) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965308169240616144800 · Cited by 6

Examining Patients Length of Stay Estimation with Explainable Artificial Intelligence Methods

Arslanoğlu, K., & Karaköse, M. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-71125-1_25 · Cited by 3

Should We Trust the Credit Decisions Provided by Machine Learning Models?

Alonso-Robisco, A., & Carbó, J. M. (2025) · Computational Economics · doi:10.1007/s10614-025-10855-x · Cited by 7

A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality

Zhu, J., Wu, X., Yu, L., & Zhang, X. (2025) · Technological and Economic Development of Economy · doi:10.3846/tede.2025.23060 · Cited by 3

Towards White-Box IDS: Integrating Explainability in IoT Ecosystems

Tewari, T., & Singal, G. (2025) · National Conference on Communications · doi:10.1109/ncc63735.2025.10983550 · Cited by 3

Delving into Human Factors through LSTM by Navigating Environmental Complexity Factors within Use Case Points for Digital Enterprises

Ranković, N., & Ranković, D. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19010020 · Cited by 3

Adversarial Robustness and Explainability of Machine Learning Models

Gafur, J., Goddard, S., & Lai, W. (2024) · Practice and Experience in Advanced Research Computing · doi:10.1145/3626203.3670522 · Cited by 3

Explainable artificial intelligence in deep learning neural nets-based digital images analysis

Averkin, A. N., Volkov, E. N., & Yarushev, S. (2024) · Известия Российской академии наук Теория и системы управления · doi:10.31857/s0002338824010122 · Cited by 4

Advanced Anemia Classification Using Comprehensive Hematological Profiles and Explainable Machine Learning Approaches

Noviandy, T. R., Idroes, G. M., Suhendra, R., Bakri, T. K., & Idroes, R. (2024) · Infolitika Journal of Data Science · doi:10.60084/ijds.v2i2.237 · Cited by 3

A preliminary investigation of diabetic nephropathy biomarkers through machine learning approaches

Cortese, N., Cutruzzolà, A., Procopio, A., Zaffino, P., Scalzi, C., Gnasso, A., Irace, C., & Cosentino, C. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821982 · Cited by 3

Artificial Intelligence and Cybersecurity: Challenges, Opportunities, and Defensive Techniques

Akpabio, E. I., & Narad, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-9523-9_25 · Cited by 3

Meta-Heuristic Feature Optimization for Predictive Analysis on HRV Dataset and Semantic Knowledge Representation for Stress Management: A Case-Study Towards Ethical AIPreprint

Ganesh, K., Chatterjee, A., Riegler, M. A., & Halvorsen, P. (2023) · Research Square · doi:10.21203/rs.3.rs-3114142/v4 · Cited by 3

Explainable AI for Predicting User Behavior in Digital Advertising

Al-Khafaji, A., & Karan, O. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-56728-5_43 · Cited by 2

Audit Program on Artificial Intelligence (AI) driven Credit Risk

Unicredit, Palma, V. A. D., Maria, A. D., Unicredit, Foschini, D., Unicredit, Frasca, V., Unicredit, Girardi, D., & Unicredit (2025) · Risk Management Magazine · doi:10.47473/2020rmm0149 · Cited by 2

A comprehensive analysis of BMI prediction using machine learning and biochemical markers: Insights from NHANES data

Chen, X., Zhu, B., Jiang, H., Lan, H., Xie, C., Lin, H., Chen, Y., & Chen, H. (2025) · Medicine · doi:10.1097/md.0000000000042781 · Cited by 2

Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms

Hua, S., Li, C., Wang, Y., Liang, Y., Xu, S., Kong, J., Gong, H., Dong, R., Lin, Y., Xu, L., et al. (2025) · BMC Anesthesiology · doi:10.1186/s12871-025-03259-9 · Cited by 2

Toward Trustworthy Multi-View Representation With Fine-Grained Explainability Embeddings

Zhang, J., Yang, Y. X., Shang, M., Guo, L., Zhang, D., & Du, L. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3607141 · Cited by 2

Patterns, drivers and projections of carbon footprints in urban Chinese households: Based on interdisciplinary approaches

Hao, H., Guo, X., Wang, C., & Yang, J. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127436 · Cited by 2

Network Centrality Feature Augmentation and SHAP Analysis of Inlier and Outlier Borrowers in Credit Scoring

Anusha Hegde, H., & Bhowmik, B. R. (2026) · Computational Economics · doi:10.1007/s10614-025-11292-6 · Cited by 2

Developing a multi-task quantitative structure-activity relationship (QSAR) model for predicting the toxicity potential of chemicals used in e-cigarette products

Canchola, A., Chen, K., Rahman, M. Z., Tran, L. N., Woo, W., Tian, L., Lin, Y., & Chou, W. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.141164 · Cited by 2

Social determinants of health to predict health-related quality of life in diabetes patients: explainable machine learning approaches

Wang, Y., Islam, M. M., Okere, A. N., Poly, T. N., & Lin, M. (2026) · Endocrine · doi:10.1007/s12020-025-04514-2 · Cited by 2

Explainable AI-driven expert system for impact profiling of contract modifications in construction projects

Oh, J., & Ashuri, B. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132030 · Cited by 2

Physical activity and sitting time as correlates of cardiometabolic multimorbidity risk in U.S. adults: an explainable machine learning classification model using SHAP and LIME

Yang, Y., Guo, Z., Dong, L., Wu, B., Xu, C., Cai, M., & Wang, Z. (2026) · BMC Public Health · doi:10.1186/s12889-026-27177-8 · Cited by 2

ReSHAPe: A redundancy-reduced SHAP-based feature selection pipeline for interpretable radiomics in biomedical image analysis

Perniciano, A., Cau, F. M., Spano, L. D., Ruberto, C. D., & Loddo, A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133854 · Cited by 2

Machine Learning-Based Migraine Classification Using Structured Clinical and Neurological Features

Iqbal, M. S., Hussain, M. I., Chowdhury, S. H., Khan, M. S., Hossain, M. M., & Mamun, M. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11546487 · Cited by 2

Explainability Requirements for Time Series Forecasts: A Study in the Energy Domain

Droste, J., Fuchs, R., Deters, H., Klünder, J., & Schneider, K. A. (2024) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re59067.2024.00030 · Cited by 2

Establishing machine-learning approach for predicting outer-diametral strains in ferritic/martensitic (F/M) steel tubes during in-reactor neutron-irradiation creep experiments

Sai, N. J., Sridharan, K., & Chauhan, A. (2024) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2024.105418 · Cited by 2

Explainable Ensemble Machine Learning Method for Credit Risk Classification

Ghozzi, S. B., HajKacem, M. A. B., & Essoussi, N. (2024) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista62901.2024.10683830 · Cited by 2

Galactose-Induced Cataracts in Rats: A Machine Learning Analysis

Al-Mashhadani, A. J. M., Gong, Q., Shehaj, F., & Zhou, L. (2025) · International Journal of Medical Sciences · doi:10.7150/ijms.103892 · Cited by 2

AI Risk Measurement

Giudici, P. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_79 · Cited by 2

Research on Personal Credit Risk Assessment Based on Combination Weight and Shap Interpretable Machine Learning

Peng, X. (2022) · Academic Journal of Computing & Information Science · doi:10.25236/ajcis.2022.051009 · Cited by 2

Computational Evaluation of Model-Agnostic Explainable AI Using Local Feature Importance in Healthcare

Erdeniz, S. P., Schrempf, M., Kramer, D., Rainer, P. P., Felfernig, A., Tran, T., Burgstaller, T., & Lubos, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34344-5_14 · Cited by 2

The Importance of Interpretability in AI Systems and Its Implications for Deep Learning

Adnan, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1738-9.ch003 · Cited by 2

Application of Feature Engineering and Ensemble Techniques on Medical End-devices for Geriatric Fall DetectionPreprint

Nandi, P., Anupama, K. R., Agarwal, H., Jain, A., Paliwal, S., Jakhar, R., Rajkhowa, R., & Guruprasanna, P. B. (2023) · Preprints.org · doi:10.20944/preprints202306.0804.v1 · Cited by 2

Credit Scoring Models: Which Performance Metrics for Optimal Financial Decision-Making?Preprint

Dessain, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4624501 · Cited by 2

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

Kamis, A. A., Gadia, N., Luo, Z., Ng, S. X., & Thumbar, M. (2024) · JMIR AI · doi:10.2196/58455 · Cited by 3

Enhancing Pneumonia Detection Transparency: Exploring Explainable AI Model

Ambaliya, M., Chauhan, S., Paliwal, M., Shastri, A., & Sabale, K. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-6710-6_35 · Cited by 1

Machine learning discrimination of Gleason scores below GG3 and above GG4 for HSPC patients diagnosis

Zhu, B., Dai, L., Wang, H., Zhang, K., Zhang, C., Wang, Y., Yin, F., Li, J., Ning, E., Wang, Q., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77033-1 · Cited by 1

Loan Forecasting Based on Machine Learning and Variable Correlation Analysis

Cui, B. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/2024.18131 · Cited by 1

Stroke Management and Analysis Risk Tool (SMART): An interpretable clinical application for diabetes-related stroke prediction

Sun, Y., Li, J., He, H., Xing, G., Liu, Z., Meng, Q., Xu, M., Huang, L., Pan, Z., Liao, J., et al. (2024) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2024.103841 · Cited by 1

Variable Selection Algorithm for Explaining Anomalies in Real-World Regenerative Thermal Oxidizers

Seo, M., & Kim, M. H. (2025) · Applied Artificial Intelligence · doi:10.1080/08839514.2025.2462374 · Cited by 1

Towards Interpretable Credit Risk Assessment: A Comparative Study of XAI Techniques with Regulatory Compliance

Chavare, C. R. D. C. D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20258383

Explainable Credit Risk Assessment: Comparing Logistic Regression, XGBoost, and LightGBM with SHAP and LIME Analysis

Imanova, H. K. D. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20484846

Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive modelsPreprint

Biecek, P., Chlebuś, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D., Sztachelski, J., & Wojewnik, P. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2104.06735 · Cited by 10

Benchmarking Explainability Methods for SME Credit Risk Models in Emerging Markets

Chowdhury, S., & Das, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32729-1_33

Explainable AI Using h2o AutoML and Robustness Check in Credit Risk Management

Sikha, S., & Vijayakumar, A. (2023) · International Conference on Intelligent Computing and Control for Engineering and Business Systems (ICCEBS) · doi:10.1109/iccebs58601.2023.10449100 · Cited by 2