CVDPredict

• Master Project by Laxmi Sowjanya Doddi

CVDPredict focuses on leveraging machine learning and data visualization techniques to predict cardiovascular disease (CVD) risks. With cardiovascular diseases being the leading global cause of death, this project aims to develop predictive models that can assess the likelihood of heart attacks based on various risk factors, empowering better decision-making and healthcare interventions.

Objectives

• Predictive Modeling: Develop machine learning models, such as logistic regression and decision trees, to forecast heart attack risks using a dataset of over 9,000 patients.
• Performance Evaluation: Compare the accuracy and reliability of machine learning predictions.

CVDPredict bridges the gap between predictive modeling and actionable insights, aiming to reduce the impact of cardiovascular diseases through data-driven healthcare solutions. This project demonstrates how the integration of machine learning and visualization tools can enhance the understanding and prevention of life-threatening conditions.