Dementia Risk Prediction Platform

Overview

This application employs AWS, SQL, and RShiny to create a comprehensive platform used to predict whether an individual has a high risk of dementia. The project aggregates 180 GB of data to create a detailed view of 180,000 individuals and their potential comorbidities.

Features

• Data Aggregation: Compiled data from 180,000 individuals, encompassing a variety of health parameters and potential comorbidities.
• AWS Integration: Designed an AWS architecture to facilitate cost-effective queries to the database.
• Machine Learning: Implemented a Random Forest model to combine different parameters and ensure accurate prediction of dementia risk.
• RShiny Interface: Developed an RShiny application to display results and facilitate queries to the AWS database.

Project Structure

• main.py: Contains the Random Forest model and related scripts.
• app.py: Contains the RShiny application code.
• AlterDataMySQL.py: Includes SQL scripts for database setup and queries.

Setup Instructions

Prerequisites

• AWS account with necessary permissions
• R and RShiny installed
• Python installed for running data aggregation and machine learning scripts
• SQL database (e.g., MySQL, PostgreSQL)

Usage

1. Open the RShiny application in your web browser.
2. Use the interface to input individual health parameters and query the AWS database.
3. View the dementia risk prediction results displayed by the RShiny application.

Contributors

Nathan Palamuttam

For detailed documentation and additional resources, please refer to the individual directories and files within this repository.