No-Code ML Approach to Predict Heart Disease with Amazon SageMaker Canvas

The article demonstrates how to use Amazon SageMaker Canvas to create a no-code machine learning model for predicting heart disease, focusing on early detection through chest pain analysis.

• Used UCI Machine Learning Repository dataset to build predictive model
• Leveraged Amazon SageMaker Data Wrangler for data preparation
• Transformed multi-class chest pain classification to binary prediction
• Improved model performance with precision of 0.825 and recall of 0.800
• Demonstrated how to use no-code ML tools for healthcare analytics

The project showcases how AWS SageMaker Canvas can democratize machine learning for healthcare professionals, enabling quick and accurate disease prediction without extensive coding expertise.