Integrating advanced deep reinforcement learning and explainable AI for enhanced pulmonary disease detection

This study investigates a significant issue within the contemporary healthcare system: identifying pulmonary embolism (PE) as a possible cause of heart failure. Given the complexity and importance of this relationship, there is an increasing demand for AI solutions that offer not only accuracy but also transparency. To tackle this issue, we are proposing a hybrid strategy that integrates advanced deep reinforcement learning methods—namely Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization (PPO)—with the Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance, along with Local Interpretable Model-agnostic Explanations (LIME) to guarantee the transparency and reliability of our predictions. Furthermore, to overcome the limitations of insufficient annotated PE-specific data, valuable features were extracted from publicly accessible cardiovascular datasets, utilising calculated metrics such as pulse pressure and stroke volume. These features were used to determine whether PE was present or absent in relation to heart failure. Our findings indicated that both A3C and PPO models demonstrated strong performance in classification, with PPO slightly surpassing A3C in terms of accuracy (A3C: 0.941, PPO: 0.958), F1-scores (A3C: 0.822, PPO: 0.864), Recall (A3C: 0.859, PPO: 0.100) and Precision (A3C: 0.736, PPO: 0.761). The incorporation of LIME enabled us to produce case-specific explanations that enhanced the clinical utility of our models. Our proposed approach presents a novel and interpretable framework for the early identification of heart failure caused by pulmonary embolism, which has considerable potential for aiding in clinical decision-making and ultimately improving patient health outcomes in realistic settings.