Machine Learning Approaches for Cardiovascular Disease Prediction and Detection Modelling

As cardiovascular disease (CVD) continues to be a major source of morbidity and mortality globally, there is an increasing need for efficient diagnostic techniques. In this field, machine learning (ML) has potential for creating models that can accurately and minimally obstruct the identification of individuals who are in risk. ML has been utilized to identify the CVD at its earlier stage. Using the UCI Heart Disease Dataset, this study assesses a number of machine learning methods, such as Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), to predict the presence of heart disease. Accuracy, precision, recall, and F1-score were used to identify the effectiveness of the model’s performance. With an accuracy of 83.4, balanced precision and strong recall, KNN is appropriate for high-sensitivity applications. In contrast, SVM demonstrated a high recall and a considerably lower accuracy of 81.5, which makes it useful for detecting true positives in medical situations. These findings demonstrate the value of machine learning in the prediction of CVD and show how carefully designed algorithms can aid in early intervention and individualized treatment planning. The complexity has been reduced by using the proposed algorithm. Future research will concentrate on improving the model’s interpretability and including bigger datasets for additional verification.