Explainable Artificial Intelligence Method for Identifying Cardiovascular Disease with a Combination CNN-XG-Boost Framework

Cardiovascular disease (CVD) is a globally significant health issue that presents with a multitude of risk factors and complex physiology, making early detection, avoidance, and effective management a challenge. Early detection is essential for effective treatment of CVD, and typical approaches involve an integrated strategy that includes lifestyle modifications like exercise and diet, medications to control risk factors like high blood pressure and cholesterol, interventions like angioplasties or bypass surgery in extreme cases, and ongoing surveillance to prevent complications and promote heart function. Traditional approaches often rely on manual interpretation, which is time-consuming and prone to error. In this paper, proposed study uses an automated detection method using machine learning. The CNN and XGBoost algorithms' greatest characteristics are combined in the hybrid technique. CNN is excellent in identifying pertinent features from medical images, while XGBoost performs well with tabular data. By including these strategies, the model's robustness and precision in predicting CVD are both increased. Furthermore, data normalization techniques are employed to confirm the accuracy and consistency of the model's projections. By standardizing the input data, the normalization procedure lowers variability and increases the model's ability to extrapolate across instances. This work explores a novel approach to CVD detection using a CNN/XGBoost hybrid model. The hybrid CNN-XGBoost and explainable AI system has undergone extensive testing and validation, and its performance in accurately detecting CVD is encouraging. Due to its ease of use and effectiveness, this technique may be applied in clinical settings, potentially assisting medical professionals in the prompt assessment and care of patients with cardiovascular disease.