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CGRPF utilizes mean imputation for missing data, the Fox Wolf Optimizer (FWO) for effective feature selection, and an AttentiveLSTM to capture temporal patterns in time-series data, aiding chronic disease prediction. Fully connected layers and a softmax layer enhance model performance and ensure calibrated classification into high, medium, and low-risk categories. Tested on the CFTR-2 dataset, CGRPF achieved strong performance metrics \u2013 97\u202f% accuracy, 91\u202f% precision, 97\u202f% recall, 93\u202f% F1-score, outperforming state-of-the-art models in CF risk prediction.<\/jats:p>","DOI":"10.1515\/sagmb-2025-0042","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:19:53Z","timestamp":1771269593000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-driven risk prediction and\u00a0categorization in\u00a0cystic fibrosis leveraging AttentiveLSTM and\u00a0Fox Wolf Optimizer"],"prefix":"10.1515","volume":"25","author":[{"given":"Ashwini A.","family":"Pandagale","sequence":"first","affiliation":[{"name":"Department of Computer Engineering , 210030 Smt Kashibai Navale College of Engineering, SPPU , Pune , Maharashtra , 411041 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lalit V.","family":"Patil","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering , 210030 Smt Kashibai Navale College of Engineering, SPPU , Pune , Maharashtra , 411041 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"2026021619194560788_j_sagmb-2025-0042_ref_011","unstructured":"Alaa, A.M. and van der Schaar, M. 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