A Hybrid Deep Learning and Advanced Optimization Approach for Lung Disease Prediction

Abstract

Lung diseases represent a major global health challenge because their symptoms can be diverse, disease progression can vary among individuals, and delayed identification may increase the risk of severe complications. Conventional diagnostic approaches often depend on clinical expertise, laboratory investigations, and imaging-based assessment, which may not always be readily available in resource-constrained environments. Recent developments in artificial intelligence have created opportunities to support disease prediction through data-driven computational models. This research proposes a hybrid deep learning and advanced optimization approach for lung disease prediction using numerical and clinical data rather than medical images. The proposed framework integrates data preprocessing, feature engineering, deep learning-based prediction, and optimization-driven model improvement into a unified predictive architecture. Deep learning models are employed to learn complex nonlinear relationships among clinical and physiological attributes, while advanced optimization techniques are utilized to identify suitable model parameters and improve predictive performance. The framework can incorporate optimization methods such as Particle Swarm Optimization, Genetic Algorithm, Differential Evolution, Grey Wolf Optimization, or other suitable metaheuristic approaches. The performance of the proposed model can be evaluated using accuracy, precision, recall, F1-score, specificity, sensitivity, ROC-AUC, and computational time. Comparative evaluation with conventional machine learning and non-optimized deep learning models can demonstrate the effectiveness of the proposed approach. The proposed framework aims to provide an efficient, reliable, and data-driven decision-support mechanism for early lung disease prediction.