# Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets

arXiv: https://arxiv.org/abs/2006.04937 · HTML: https://mlsanity.com/paper/2006.04937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History](https://mlsanity.com/paper/2304.09322/) (2023)

## Abstract (arXiv)

Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. In this paper, we consider fast Raman spectroscopy data and demonstrate that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.