Machine Learning-Based Prediction of Cardiovascular Disease Using Machine Learning Algorithm

Cardiovascular disease is a leading cause of death worldwide, necessitating accurate and timely diagnosis to guarantee successful treatment. Traditional diagnostic methods are hampered by the complexity of cardiac disease and its many components. This paper proposes a machine learning-based strategy for predicting cardiac disease that employs classification algorithms such as Neighbor (KNN) and Support Vector Machine. This model investigated and tested a variety of data, including age, blood pressure, cholesterol levels, and ECG. To boost the model's effectiveness, the methodology comprises preparatory data processing systems such as standardization and selection signs. To mend the model's performance, a systematic hyper parameter tuning approach is employed. The model is based on accuracy, review, and measurements like the F1-Indicator.According to the experiment data, the KNN model reaches 97%of the accuracy, indicating that the predictive potential is higher than that of traditional diagnostic approaches. In addition, the stability of the model confirms the reliability in the actual application and avoids the risk of experience. This study shows the promise of machine learning that supports clinical solutions and provides extended, effective and non -invasive diagnostic methods. Future research aims to increase real -time monitoring and deep learning architecture to increase the predicted accuracy and medical services