Prediction of Aortic Aneurysm Disease Using Supervised Machine Learning Algorithms

The prediction of aortic aneurysm, a potentially fatal type of cardiovascular disease (CVD), has become a significant focus in healthcare due to its global impact. This research utilizes an ensemble of supervised learning techniques – Naive Bayes, Logistic Regression, Random Forest, Support Vector Machines (SVM), and Factor Analysis to predict the likelihood of aortic aneurysm based on patient data. The study analyzes Aortic aneurysm disease dataset, comparing the performance of these algorithms in terms of accuracy, precision, recall, and F1-score. Results show that Random Forest outperformed other models with an accuracy of 82%, followed by SVM with 79%, Logistic Regression with 76%, Factor Analysis with 74%, and Naive Bayes with 72%. These findings highlight the efficacy of machine learning algorithms in healthcare analytics but specifically aortic aneurysm disease prediction.