What question did this study set out to answer?

The aim is to develop an AI-driven framework for accurate risk prediction in cystic fibrosis.

February 19, 2026Statistical Applications in Genetics and Molecular Biology0 citations

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer

Key Points

The aim is to develop an AI-driven framework for accurate risk prediction in cystic fibrosis.
Proposed the Cystic Fibrosis Risk Prediction Framework (CGRPF).
Utilized mean imputation to handle missing data.
Employed Fox Wolf Optimizer for feature selection.
Implemented AttentiveLSTM to analyze temporal patterns in data.
Tested performance on the CFTR-2 dataset.
Achieved 97% accuracy and 91% precision in risk classification.
Delivered 97% recall and 93% F1-score.
Outperformed existing models in predicting cystic fibrosis risk.

Abstract

Abstract Cystic fibrosis (CF), a genetic disorder stemming from CFTR gene mutations, requires accurate risk prediction to improve management. Modulator therapies have advanced treatment but remain limited, as they don’t cover all gene variants and face accessibility issues. To address these challenges, a novel Cystic Fibrosis Risk Prediction Framework (CGRPF) is proposed. 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 – 97 % accuracy, 91 % precision, 97 % recall, 93 % F1-score, outperforming state-of-the-art models in CF risk prediction.

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