“An Explainable Hybrid LSTM–Random Forest Framework for Accurate Pulmonary Disease Detection and Classification”

Abstract: Pulmonary diseases such as Chronic Obstructive Pulmonary Disease (COPD), pneumonia, and lung cancer continue to be leading causes of global morbidity and mortality. Timely detection and accurate diagnosis are essential for effective treatment and improved clinical outcomes. Traditional diagnostic techniques—relying heavily on chest X- rays and CT scans—are often constrained by manual interpretation, which is time-consuming and susceptible to human error. This paper proposes a novel hybrid diagnostic framework integrating Long Short-Term Memory (LSTM) networks with Random Forest (RF) ensemble learning to improve the detection and classification of pulmonary conditions. LSTM networks are employed to capture temporal dependencies in sequential clinical data, while the RF model enhances classification robustness and accuracy. The proposed approach includes comprehensive preprocessing of medical imaging and structured clinical data, feature extraction, and model training on an extensive annotated dataset. Evaluation metrics such as accuracy, sensitivity, specificity, and F1-score reveal that the LSTM-RF hybrid outperforms conventional machine learning models. Furthermore, Explainable AI (XAI) techniques are incorporated to ensure model interpretability, promoting transparency in clinical decision-making. The study also highlights real-world deployment challenges, including data privacy, algorithmic bias, and regulatory compliance. The key contributions of this research lie in the integration of deep learning with ensemble techniques and the emphasis on explainability, making it a viable solution for real-time pulmonary disease diagnosis in clinical settings.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Pulmonary Disease Detection, Hybrid LSTM, Random Forest, Explainable AI, XAI, COPD, CT scan.