Unsupervised Online Feature Selection for Cost-Sensitive Medical Diagnosis

TL;DR

This paper introduces an unsupervised online feature selection method tailored for cost-sensitive medical diagnosis, enabling cost-effective and accurate prediction without knowing the true patient state.

Contribution

It formulates the medical diagnosis as a cost-sensitive feature selection problem and develops online algorithms leveraging the Weak Dominance property for optimal feature trade-offs.

Findings

Algorithms perform well on real-world datasets

Effective trade-off between cost and accuracy achieved

No prior knowledge of true patient state required

Abstract

In medical diagnosis, physicians predict the state of a patient by checking measurements (features) obtained from a sequence of tests, e.g., blood test, urine test, followed by invasive tests. As tests are often costly, one would like to obtain only those features (tests) that can establish the presence or absence of the state conclusively. Another aspect of medical diagnosis is that we are often faced with unsupervised prediction tasks as the true state of the patients may not be known. Motivated by such medical diagnosis problems, we consider a {\it Cost-Sensitive Medical Diagnosis} (CSMD) problem, where the true state of patients is unknown. We formulate the CSMD problem as a feature selection problem where each test gives a feature that can be used in a prediction model. Our objective is to learn strategies for selecting the features that give the best trade-off between accuracy and costs. We exploit the `Weak Dominance' property of problem to develop online algorithms that identify a set of features which provides an `optimal' trade-off between cost and accuracy of prediction without requiring to know the true state of the medical condition. Our empirical results validate the performance of our algorithms on problem instances generated from real-world datasets.