Kidney Stone Risk Prediction Using CNNs

This project leverages deep learning, specifically Convolutional Neural Networks (CNNs), to predict susceptibility to kidney stones based on genotypic data. We process genetic variants from over 400 single nucleotide polymorphisms (SNPs) and use these to generate Polygenic Risk Scores (PRS) for a cohort of 60 individuals. The CNN model is designed to capture complex patterns in the genetic data to distinguish between high-risk and low-risk individuals effectively. Evaluation metrics include accuracy, ROC-AUC, precision, and recall to assess model performance.

Note: This project is in the very early stages and is part of a senior thesis at Case Western Reserve University (CWRU).