  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.
