Interpretable Parkinson's Disease Detection Using Group-Wise Scaling
This study is aimed at detecting Parkinson's disease by analyzing voice measurements made using a mobile phone. The key objectives include creating a model that ensures accurate predictions while maintaining interpretability, consistent with the existing literature on Parkinson's disease. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the dataset
