Cardiovascular Disease Prediction with Machine Learning

Abstract

Cardiovascular disease (CVD) is a significant global health concern and a leading cause of death. A machine learning odel is the best predictor for the early detection and accurate prediction and prevention of CVD and the best prospects for the study. We begin by creating and segmenting datasets rich in health-related characteristics carefully. We play an essential role in preserving accuracy with data processing models that power data transformation, feature selection, and eliminating outliers. By verifying these steps, the dataset is carefully prepared for predictive modelling purposes. Various models such as XGBoost, Logistic Regression, LightGBM, K-Nearest Neighbors, Gaussian Naive Bayes, Random Forest, Decision Tree, Extra Tree, AdaBoost, Gradient Boosting, Support Vector Machine, and CatBoost are tested before searching for CVD. Our Analysis refers to teaching and testing accuracy to determine the model's performance and generalization ability. CatBoost Classifier has been recognized as an expert performer, demonstrating exceptional test accuracy [1] and literacy when applied to unfamiliar data. However, we analyze the essential features and provide valuable insight into factors in CVD prediction, which positively influence the prognosis of CVD disease. If CatBoost exhibits state-of-the-art accuracy levels, the hyperparameter tuning [2] offers more capability, representing a promising avenue for future research efforts. In summary, this study summarizes, augments, and improves the application of machine learning in cardiovascular disease prediction. These results are not only useful for healthcare practitioners and researchers but also underscore the importance of AI, data processing, and feature selection in healthcare analytics. This work establishes a solid foundation for future research efforts, which join the advancement of medical science and the exploration of healthcare avenues.