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Dementia Prediction Model

Overview

Hello! This project was created as part of a group research effort to explore the impact of technology use on brain health, with a particular focus on dementia. As reliance on technology continues to grow, we noticed how increased screen time and reduced attention to mental well-being could negatively affect the brain. This observation sparked our curiosity and motivated us to research dementia and build a prediction model to evaluate the likelihood of dementia in a set of test subjects.

Motivation

We wanted to investigate whether modern lifestyles may correlate with higher dementia risks. Our project combines research and hands-on experimentation, giving us the opportunity to apply machine learning techniques to a real-world problem that affects millions of people worldwide.

Methodology

  • Platform: Google Colab
  • Languages: Python
  • Libraries & Tools: TensorFlow, NumPy, Pandas
  • Approach:
    • Data preprocessing and cleaning
    • Feature selection and normalization
    • Model training with TensorFlow
    • Evaluation with accuracy, precision, and recall metrics

Results

Our prediction model was able to identify patterns in the dataset and provide predictions about dementia likelihood. While this project was primarily exploratory, the results highlight the potential of machine learning in healthcare applications.

Future Work

  • Expand the dataset for greater reliability
  • Experiment with different deep learning architectures
  • Incorporate lifestyle and environmental variables for richer insights
  • Deploy the model into an interactive web app for demonstration purposes

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A predictive model estimating the likelihood of dementia in test subjects.

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