Cardiovascular Syndrome Prediction Using Machine Learning Algorithms - Sreenivasulu, K.; Anuradha, B.; Reddy, A. Chandra Obula; Neerugatti, Vikram; Jayanthi, Appawala; Baseer, K. K.; Dhabliya, Dharmesh (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Cardiovascular disease can be caused by a variety of factors. Researchers can predict cardiovascular infirmity using a variety of methods, regardless of whether a person has the condition or not. The heart disease has been placed via extracting significant qualities and most relevant features using a variety of research methods, such as pulse, cholesterol levels, and other symptoms. The major goal of the study is to use the data to forecast whether the person has a cardiovascular condition. As a result, data mining is employed, which makes it simple to analyse the data collection. Null values and duplicate values are eliminated. The data is subjected to regression analyses utilising decision trees with party and rpart, random forests, linear regression, and logistic regression. The data set is trained and tested using regressions. The regressions are compared, and the outcome for the data set is reliable. All comparisons within the data set are then made using the regression. Therefore, the findings indicate whether or not the individual will eventually develop cardiovascular disease.