Alzheimer’s is a complex neurodegenerative disease that develops slowly and is difficult to detect early. Inspired by the need for accessible computational health tools, I created the AI4Alzheimer Dashboard, which allows students and researchers to explore genetic and biomarker data and predict Alzheimer’s risk.
Through this project, I learned how to handle large genomic datasets, clean and process data in Python, visualize complex results with Plotly, and build a user-friendly interactive dashboard using Streamlit. I also integrated a machine learning model (Random Forest) to provide real-time risk predictions for new patient data.
Challenges:
Handling missing, infinite, and extreme values in genomic data.
Combining data visualization and machine learning in a single interactive dashboard.
Making the dashboard both informative for researchers and simple enough for students.
Headline: AI-powered early detection and data exploration tool for Alzheimer’s disease
Inspiration: The project was inspired by the urgent need to detect Alzheimer’s early and make computational medicine accessible to everyone, even students without lab access.
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