# Decision Making with Machine Learning and ROC Curves

## TL;DR

This paper explores the statistical properties of ROC curves in binary classification, analyzing their implications for model selection and decision-making, supported by empirical data from healthcare diagnostics.

## Contribution

It provides a theoretical analysis of ROC curve properties and examines how incentive heterogeneity and information asymmetry affect decision models.

## Key findings

- ROC curves' statistical properties influence model selection.
- Incentive heterogeneity impacts decision outcomes.
- Empirical analysis on healthcare data illustrates theoretical points.

## Abstract

The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the implications of different models of incentive heterogeneity and information asymmetry on the relation between human decisions and the ROC curves. Our theoretical discussion is illustrated in the context of a large data set of pregnancy outcomes and doctor diagnosis from the Pre-Pregnancy Checkups of reproductive age couples in Henan Province provided by the Chinese Ministry of Health.