Cardio vascular disease prediction using machine learning

Cardio vascular disease, commonly referred to as heart disease, is the major cause of death globally, responsible for an estimated 17.9 million deaths each year. This result highlights the critical importance of early detection and diagnosis to reduce mortality rates and improve patient outcomes. Traditional diagnostic approaches rely on clinical tests, physician assessments, and medical imaging, which, while effective, can be time-consuming and subject to human error or bias. In recent years, machine learning acts as a powerful tool to aid in predictive healthcare, enabling the development of models that can identify patterns in patient data and predict disease risk with high accuracy. These datasets are designed to predict the risk of CVD based on a symptoms, lifestyle factors, and medical history. Each row in the dataset represents a patient, with binary indicators for symptoms and risk factors, along with a computed risk label indicating whether the patient is at high or low risk of developing heart disease. The two algorithms’ performances were revealed.