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Traditionally, machine learning algorithms rely on the assumption that allfeatures of a given dataset are available for free. However, there are manyconcerns such as monetary data collection costs, patient discomfort in medicalprocedures, and privacy impacts of data collection that require carefulconsideration in any real-world health analytics system. An efficient solutionwould only acquire a subset of features based on the value it provides whileconsidering acquisition costs. Moreover, datasets that provide feature costsare very limited, especially in healthcare. In this paper, we provide a healthdataset as well as a method for assigning feature costs based on the totallevel of inconvenience asking for each feature entails. Furthermore, based onthe suggested dataset, we provide a comparison of recent and state-of-the-artapproaches to cost-sensitive feature acquisition and learning. Specifically, weanalyze the performance of major sensitivity-based and reinforcement learningbased methods in the literature on three different problems in the healthdomain, including diabetes, heart disease, and hypertension classification.

Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data