Machine Learning-Based Cardiovascular Disease Prediction: A Comparative Analysis of Classification Algorithms for Enhanced Diagnostic Accuracy

Cardiovascular diseases (CVDs) were one of the major causes of death in all the continent, to such an extent that early detection and accurate diagnosis of CVDs is crucial to the early intervention. The aim of this research is to predict the likely hood of a heart related condition based on ML techniques in order to increase diagnostic precision. To develop predictive models, a dataset of 383 patient records along with 13 key attributes such as age, cholesterol levels, blood pressure, heart rate was used. The implementation of various ML algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Logistic Regression in Python was done with the help of Jupyter Notebook environment. And the study focuses on data preprocessing, feature selection, and algorithm performance evaluation by means of accuracy, precision, recall, and F1-score. SVM was found to be the model with the highest accuracy of 86% on the tested models, which shows its ability to cope with the complexity of clinical data. In consequence, it also emphasizes the promise of ML driven predictive systems for timely, data driven decision support in the clinical setting, and in so doing, improves patient outcomes.