Abstract
Cardiovascular diseases (CVDs) are a major cause of death worldwide, ranking among the deadliest disease. By utilizing statistical and machine learning (ML) algorithms to discover risk biomarkers, CVDs can be early detected and prevented. In this work, we use biochemical data and clinical CVD risk factors to predict CVD-related death within a 10-year follow-up period using machine learning models like Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB), and Adaptive Boosting (AdaBoost). Using the Ludwigshafen Risk and Cardiovascular Health (LURIC) study cohort, we included 2943 individuals in our analysis, of whom 484 were declared deceased from cardiovascular disease. For every model, we determined its accuracy (ACC), precision, recall, F1-score, specificity (SPE), and area under the receiver operating characteristic curve (AUC). According to the comparative analysis's results, the most dependable algorithm is logistic regression, which has an accuracy of 72.20%. In the TIMELY trial, these findings will be utilized to calculate the risk score and mortality of cardiovascular disease in patients with a 10-year risk.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
More From: INTERNATIONAL JOURNAL OF RESEARCH IN MEDICAL SCIENCES & TECHNOLOGY
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
