Diseases such as heart attack, breast cancer, and diabetes have a significant impact on public health. Early detection and accurate prediction of these diseases play a crucial role in improving outcomes for individuals. Leveraging the power of machine learning, the ML-HealthCare project aims to provide a web-based application that visualizes different aspects of these diseases and predicts the likelihood of an individual having them. The ML-HealthCare project is a web-based application developed using Python's Streamlit library. It offers a user-friendly interface where individuals can explore disease-related information and receive predictions based on their input parameters. The project focuses on three major diseases: heart attack, breast cancer, and diabetes.
- Develop a web-based application using the Streamlit library to provide an interactive platform for disease analysis and prediction.
- Visualize and analyze various aspects of heart attack, breast cancer, and diabetes through interactive charts, graphs, and data visualizations.
- Implement machine learning models to predict the likelihood of individuals having heart attack, breast cancer, or diabetes based on user-provided input parameters.
- Contribute to early detection and prevention of heart attack, breast cancer, and diabetes, ultimately improving public health outcomes.
- Heart Attack
- Diabetes
- Breast Cancer
- Decision Tree
- Logistic Regression
- Random Forest
- Gradient Boosting
- XGBoost
- Support Vector Machine
- K Nearest Neighbour
The system proposed includes increased disease awareness through interactive visualizations, timely detection of heart attack, breast cancer, and diabetes through machine learning-based predictions, and the empowerment of individuals by providing personalized risk assessments and informed decision-making regarding their health.
