AI CARDIOLOGIST ADVANCEMENTS IN SUPERVISED LEARNING FOR HEART DISEASE PREDICTION

Heart disease is the biggest problem in the world and having no age to occur it can occur to a man or even a kid or any healthy person with the problems in their genes. Early and accurate prediction is crucial for effective preventive measures. This project explores the transformative potential of supervised learning within Artificial Intelligence (AI) for heart disease prediction. We used many algorithms like KNN and Tree algorithm to study more about the disease-predicting technology. The project examines how AI cardiologists, by using the algorithms can able to take the data of the user or a patient and can help the patient to get a review of the disease and the stage they were in, This type of analysis holds trust for more accurate and personalized risk assessments, leading that resulted in data to get a new outcome on how to prevent the problem. We understand all of the challenges of data quality, bias mitigation, and explainability in AI models, emphasizing the importance of ethical considerations in their development and deployment. Finally, the paper discusses the future directions of AI-powered heart disease prediction, exploring the potential of emerging techniques like explainable AI and federated learning to advance this field further. Keywords: Cardiovascular disease (CVD) prediction, Supervised learning, Artificial Intelligence (AI), Heart disease, Algorithms (logistic regression, support vector machine)