Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models
Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance.
Authors
- Kumbhar Mahesh
- B. Vaishnavi (ORCID: https://orcid.org/0009-0006-3667-595X)
- P K Aishwarya
- Shanmugasundaram Hariharan (ORCID: https://orcid.org/0000-0001-9686-4329)
- S Nithin
- S. Vasantha
Publication Details
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/ic363308.2025.10956662
- Primary Topic
- Cardiac Health and Mental Health
- Type
- article
- Field-Weighted Citation Impact
- 0.00