Implementation of Machine Learning Approach for Detecting Cardiovascular Diseases

The rapidly expanding discipline of data analysis has an important role to play in the medical industry. Using this knowledge, we can uncover previously concealed details that might aid in early illness prediction. Predicting cardiovascular illness is one of the most pressing issues of our day. The medical community views heart disease prediction as a challenging endeavour. Machine learning is important for the medical field's massive data training and testing needs. Creating and assessing a heart disease prediction system is crucial for early detection and treatment of the condition. This research uses a variety of machine learning methods to predict the possibility of heart illness and diagnose the patient with heart disease or not. These methods include the Decision Tree, the K - Nearest Neighbour classifier, and the Support Vector Machine. Finally, this study provides a cardiac result, and trials comparing the suggested technique to others have shown that it may be used to provide the forecast to the patient.