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The "HealthAI Predictive Suite" is an application designed to provide predictive analytics in the field of healthcare.
It encompasses multiple prediction models aimed at assisting healthcare professionals in making informed decisions and improving patient care. Here's a detailed overview of the application:
🟢 Click on the link to see the Live: HealthAI-Predictive-Suite
🟢 Click on the image to see the demo video:
- 'The primary goal of the "HealthAI Predictive Suite" is to leverage machine learning algorithms to predict various health conditions based on input data.
- Home Page: Provides an overview of the application and its functionalities. - Breast Cancer Prediction: Predicts the likelihood of breast cancer occurrence based on patient data such as age, genetic factors, and medical history. - Diabetes Prediction: Predicts the risk of diabetes development using factors like age, weight, lifestyle, and family history. - Heart Disease Prediction: Estimates the probability of heart disease occurrence based on parameters like blood pressure, cholesterol levels, and lifestyle factors. - Hypertension Prediction: Predicts the risk of hypertension (high blood pressure) based on various health indicators. - Stroke Prediction: Predicts the likelihood of stroke occurrence using factors such as age, blood pressure, smoking status, and medical history.
- Users can navigate between different prediction models using a sidebar menu or navigation buttons. The sidebar menu displays the list of available prediction models, allowing users to select the desired model for prediction.
Render is a popular platform for deploying applications due to its numerous advantages that simplify and enhance the deployment process. Here are some key benefits of using Render for deployment:
To run the web application locally, follow these steps:
Clone this repository
https://github.com/aakashmohole/HealthAI-Predictive-Suite.git
Install the necessary dependencies using
pip install -r requirements.txt
Run the Streamlit application using
streamlit run app.py
Access the application in your web browser at http://localhost:5000.
This project is licensed under the MIT License - see the LICENSE file for details.