Amalgam Based Cardiovascular Disease Prediction Using Xception with XGBoost Model

Abstract

Chronic diseases are among the most challenging healthcare issues in the world, which must be identified efficiently so that proper management and intervention may be applied. In this paper, we proposed a new hybrid model using Xception and XGBoost for cardiovascular disease predictions. The proposed model has been trained on an open-source dataset obtained from Kaggle and substantially improves precision, sensitivity, specificity, and F1 score against the existing pre-trained models. It presents an improvement of 30-40% in the performance metrics, thus proving that our hybrid model has a better prediction rate. This progress can be built by advanced data preprocessing techniques, novel implementation of algorithms, and cautious hyperparameter tuning. Rigorous testing under all kinds of conditions further proves the robustness and generalizability of our model, hence its reliability in real-world scenarios. Such high accuracy of the hybrid model in reducing false positives while increasing true positives certainly provides substantial potential for enhancing patient outcomes and efficient allocation of medical resources within clinical practice. While our study underlines the clinically correct integration of such a model into healthcare systems to allow for early diagnosis and being able to take care of patients better, it also recognizes that further investigations in future work will need to be performed concerning biases in datasets by the inclusion of more diversity in datasets, along with additional features or even machine learning techniques. Our hybrid model is thus a significant step forward for cardiovascular risk stratification, hence highly effective at improving health outcomes and healthcare efficiency.