UppuluriKalyani/ML-Nexus

Alzheimer Disease Progression Prediction

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Neurodegenerative Disease Progression Predictor

Project Overview

The Neurodegenerative Disease Progression Predictor is a machine learning-based application designed to predict the progression of neurodegenerative diseases using clinical and demographic data. It supports healthcare professionals in making informed decisions by offering insights into the likely disease progression, which helps in early intervention and personalized treatment planning.

Description

This application uses machine learning algorithms to predict disease progression based on patient-specific features like age, gender, years since diagnosis, and various health metrics. By utilizing these inputs, the model estimates the risk and severity of disease progression, helping clinicians to focus on high-risk patients and tailor treatment approaches.

Tech Stack

  • Python: Core language for data processing and model development.
  • Pandas: For data manipulation.
  • Scikit-learn: For scaling input data and building the machine learning model.
  • Streamlit: Provides an interactive web interface.
  • Pickle: Used to load the pre-trained machine learning model for real-time predictions.

Usage Instructions

  1. Run the application:

    • The application interface is hosted via Streamlit. Input patient details, including:
      • Age
      • Gender
      • Years since diagnosis
      • Health metrics
  2. Predict Progression: After filling in the required fields, click on the "Predict Progression" button to receive the predicted disease progression status.

Screenshot

Screenshot

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Hello @rakheshkrishna2005! Your issue #455 has been closed. Thank you for your contribution!