2019/02/19 by Mohammad Kachuee, Kachuee, Mohammad, Kimmo Karkkainen +8 · 2 citations
Computer Science · Health Professions · Mathematics · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Machine Learning in Healthcare #cs.AI #cs.CY #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1902.07102
openalex publication_date 2019/02/19 · arxiv created 2019/06/30 · arxiv updated 2019/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.