Research article


Dr. Ashish Mishra, Nitika Bhatt

Online First: December 13, 2022

Cardiovascular diseases (CVDs), which are heart-related illnesses, have emerged as the most hazardous disease in India and throughout the world in recent years. CVDs are the primary cause of countless deaths worldwide. Therefore, a trustworthy, accurate, and useful framework is required to analyse such illnesses in time for appropriate therapy. Different clinical datasets have been subjected to machine learning algorithms and techniques in order to automate the analysis of enormous and complex data. Recently, many analysts have started using a few AI techniques to help the healthcare sector and specialists in the diagnosis of heart-related ailments. In light of such calculations and techniques, this research examines several models and studies how they are presented. Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayes, Decision Trees (DT), and Random Forest (RF) models are among the most well-known among researchers when it comes to supervised learning algorithms. In order to recognise or diagnose cardiac disease and ascertain whether a person has it or not, this initiative offers a prediction model. By contrasting the accuracy of applying rules with the singular discoveries of Support Vector Machine, KNN classifier, Decision Tree Classifiers, and calculated relapse, the dataset used to introduce an exact model of foreseeing cardiovascular ailment is made.


Cardiovascular Diseases; Machine-Learning; Support Vector Machine(SVM); Decision Tree Classifier; KNN Classifier;LogisticRegression.