• Venkata Ramana Kaneti

Abstract: Cardiovascular disease (CVD) encompasses a range of heart and blood vessel disorders and remains a leading global cause of mortality. Annually, approximately 26 million individuals worldwide experience the impact of CVD. Early detection of cardiovascular conditions is pivotal to enabling timely intervention through counseling and medications. In this research endeavor, we aim to enhance the accuracy of heart-related disease prediction by employing machine learning models. A variety of machine learning algorithms, including logistic regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Multinomial Naive Bayes (MNB), will be utilized to build predictive models. Leveraging cardiovascular data, our models will improve the accuracy of forecasting heart failure events within a medical database, extending the scope beyond heart failure to enhance predictive capabilities for various cardiac conditions. This study introduces a machine learning-based approach that can advance disease prediction, offering valuable insights into heart-related health conditions.

Keywords: CVD, Boosting Classifiers, Ensemble Learning

"PREDICTING CARDIOVASCULAR DISEASE IN HEART PATIENTS THROUGH ENSEMBLE LEARNING MODELS AND BOOSTING CLASSIFIERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.542-550, March-2019, Available :http://www.jetir.org/papers/JETIR1903O75.pdf

"PREDICTING CARDIOVASCULAR DISEASE IN HEART PATIENTS THROUGH ENSEMBLE LEARNING MODELS AND BOOSTING CLASSIFIERS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp542-550, March-2019, Available at : http://www.jetir.org/papers/JETIR1903O75.pdf