{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T03:29:08Z","timestamp":1781148548899,"version":"3.54.1"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>\n                    Customer churn prediction is critical for telecommunications companies to maintain profitability and inform retention strategies. This study builds upon existing work by implementing a comprehensive machine learning framework using the Telco Customer Churn dataset (\n                    <jats:italic>n<\/jats:italic>\n                    = 7,043). Our methodology integrated comprehensive feature engineering, SMOTE oversampling, and training of seven machine learning models including XGBoost, Random Forest, and a Multi-layer Perceptron. Model interpretation was conducted via SHAP analysis and customer segmentation. Key results demonstrated that gradient boosting algorithms (XGBoost, LightGBM, Gradient Boosting) achieved the highest balanced performance with accuracy, precision, recall, and F1-scores of 0.84, with XGBoost attaining the best discriminative ability (AUC-ROC: 0.932). A soft-voting ensemble of the top models matched this performance (F1-score: 0.84, AUC-ROC: 0.918). SHAP analysis revealed that contract type, tenure, and technical support were the features contributing most to the model's churn predictions. Threshold optimization at 0.528 balanced precision (0.90) and recall (0.91) while reducing false negatives by 15%. The findings provide actionable insights for prioritizing high-risk customers and designing targeted retention strategies in the telecom sector.\n                  <\/jats:p>","DOI":"10.3389\/frai.2026.1748799","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T07:05:46Z","timestamp":1770707146000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable AI-driven customer churn prediction: a multi-model ensemble approach with SHAP-based feature analysis"],"prefix":"10.3389","volume":"9","author":[{"given":"Ali","family":"El Attar","sequence":"first","affiliation":[{"name":"Faculty of Computer Studies (FCS), Arab Open University (AOU)","place":["Beirut, Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"El-Hajj","sequence":"additional","affiliation":[{"name":"Faculty of Computer Studies (FCS), Arab Open University (AOU)","place":["Beirut, Lebanon"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104629","DOI":"10.1016\/j.rineng.2025.104629","article-title":"A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry","volume":"26","author":"Asif","year":"2025","journal-title":"Results Eng"},{"key":"B2","first-page":"680","article-title":"\u201cSelf-adapting cyclic oversampling for imbalanced data,\u201d","author":"Blagus","year":"2017","journal-title":"International Conference on Machine Learning and Data Mining in Pattern Recognition"},{"key":"B3","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/a17060231","article-title":"Prediction of customer churn behavior in the telecommunication industry using machine learning models","volume":"17","author":"Chang","year":"2024","journal-title":"Algorithms"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785","article-title":"\u201cXgboost: a scalable tree boosting system,\u201d","author":"Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery And Data Mining"},{"key":"B5","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/IC3SE62002.2024.10592931","article-title":"\u201cComparative analysis of predictive models for customer churn prediction in the telecommunication industry,\u201d","volume-title":"2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)","author":"Christopher","year":"2024"},{"key":"B6","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/ICAAIC56838.2023.10141019","article-title":"\u201cIntegrated customer analytics using explainability and automl for telecommunications,\u201d","volume-title":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","author":"Eswarapu","year":"2023"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","article-title":"Verification of forecasts expressed in terms of probability","volume":"78","author":"Glenn","year":"1950","journal-title":"Monthly Weather Rev"},{"key":"B8","first-page":"1321","article-title":"\u201cOn calibration of modern neural networks,\u201d","volume-title":"International Conference on Machine Learning","author":"Guo","year":"2017"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","author":"Hastie","year":"2009","journal-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.eswa.2011.08.024","article-title":"Customer churn prediction in telecommunications","volume":"39","author":"Huang","year":"2012","journal-title":"Expert Syst. 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