Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
arXiv:1902.07102
Abstract
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration in any real-world health analytics system. An efficient solution would only acquire a subset of features based on the value it provides while considering acquisition costs. Moreover, datasets that provide feature costs are very limited, especially in healthcare. In this paper, we provide a health dataset as well as a method for assigning feature costs based on the total level of inconvenience asking for each feature entails. Furthermore, based on the suggested dataset, we provide a comparison of recent and state-of-the-art approaches to cost-sensitive feature acquisition and learning. Specifically, we analyze the performance of major sensitivity-based and reinforcement learning based methods in the literature on three different problems in the health domain, including diabetes, heart disease, and hypertension classification.
References in corpus (15)
- Adam: A Method for Stochastic Optimization
- Least Angle Regression
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Rejoinder to "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
- Adaptive Classification for Prediction Under a Budget
- Dynamic Feature Acquisition Using Denoising Autoencoders
- Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification