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The framework employs ACO-based feature selection to identify significant clinical variables including renal biomarkers, electrolyte imbalance, and hemodynamic variables to enhance interpretability and computational efficiency. LSTM is trained to identify temporal relations between patient data to facilitate early and accurate prediction of AKI occurrence. Stratified data splitting was employed for unbiased evaluation to ensure robust assessment. The experiment results indicate that LSTM-ACO performs superior to conventional machine learning and deep learning algorithms including GBM, GLM, KNN, EOAEDL-CKDD, OANN and OLSTM with AUC-ROC value of 0.99, sensitivity of 95.5%, and specificity of 97.3%. ACO-based feature selection was able to reduce the dimensionality by 40% with maximum utilization of computational resources without any compromise on predictive capability. The findings point toward the potential of the LSTM-ACO model to be used in real-time clinical decision-making for the prediction of AKI in ICU settings. <\/jats:p>","DOI":"10.1142\/s0219843625400031","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T05:01:21Z","timestamp":1753246881000},"source":"Crossref","is-referenced-by-count":0,"title":["Optimized Acute Kidney Injury Monitoring: Integrating Long Short-Term Memory (LSTM) Networks with Ant Colony Optimization (ACO)"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4412-0178","authenticated-orcid":false,"given":"Archana","family":"Chaluvadi","sequence":"first","affiliation":[{"name":"Massachusetts Mutual Life Insurance Company, Springfield, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-912X","authenticated-orcid":false,"given":"Nashwan 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