# Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines

## TL;DR

This paper develops methods for selecting optimal biomarker combinations for treatment decisions by incorporating measurement costs into a feature selection framework using kernel machines, improving model efficiency and cost-effectiveness.

## Contribution

It introduces a novel feature selection approach with cost considerations for treatment-rule optimization using kernel machine techniques, addressing cost and performance trade-offs.

## Key findings

- Feature selection improves treatment rule accuracy
- Incorporating biomarker costs reduces measurement expenses
- Methods outperform existing approaches in simulations and real data

## Abstract

Optimal biomarker combinations for treatment-selection can be derived by minimizing total burden to the population caused by the targeted disease and its treatment. However, when multiple biomarkers are present, including all in the model can be expensive and hurt model performance. To remedy this, we consider feature selection in optimization by minimizing an extended total burden that additionally incorporates biomarker measurement costs. Formulating it as a 0-norm penalized weighted classification, we develop various procedures for estimating linear and nonlinear combinations. Through simulations and a real data example, we demonstrate the importance of incorporating feature-selection and marker cost when deriving treatment-selection rules.