# Classification Under Uncertainty: Data Analysis for Diagnostic Antibody Testing

arXiv: https://arxiv.org/abs/2012.10021 · HTML: https://mlsanity.com/paper/2012.10021/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Analysis of Diagnostics (Part I): Prevalence, Uncertainty Quantification, and Machine Learning](https://mlsanity.com/paper/2309.00645/) (2023)
- [Probabilistic Consistency in Machine Learning and Its Connection to Uncertainty Quantification](https://mlsanity.com/paper/2507.21670/) (2025)
- [Analysis of Diagnostics (Part II): Prevalence, Linear Independence, and Unsupervised Learning](https://mlsanity.com/paper/2408.16035/) (2024)
- [Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing](https://mlsanity.com/paper/2508.01065/) (2025)

## Abstract (arXiv)

Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either: (i) defining a third class of hold-out samples that require further testing; or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics (ROC) by connecting them to the broader field of optimization.