{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nutrition-and-health-data-for-cost-sensitive","title":"Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data","arxiv_id":"1902.07102","date":"2019-02-19","proceeding":null,"authors":["Mohammad Kachuee","Kimmo Karkkainen","Orpaz Goldstein","Davina Zamanzadeh","Majid Sarrafzadeh"],"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.","url_abs":"https://arxiv.org/abs/1902.07102v2","url_pdf":"https://arxiv.org/pdf/1902.07102v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nutrition-and-health-data-for-cost-sensitive","repo_url":"https://github.com/mkachuee/Opportunistic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"nutrition-and-health-data-for-cost-sensitive","repo_url":"https://github.com/mkachuee/DynamicFeatureAcquisition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1902.07102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07102"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mkachuee/DynamicFeatureAcquisition","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mkachuee/Opportunistic","reach":null}],"summary":{"ran_violates":1,"ran_fixture":1,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"bf606675634d383a","entry":"preproc_onehot","repo":"mkachuee/Opportunistic","repo_kind":"official","path":"nhanes.py","file_url":"https://github.com/mkachuee/Opportunistic/blob/HEAD/nhanes.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bf606675634d383a"}},{"code_sha256_prefix":"e73a57669f9b7710","entry":"preproc_real","repo":"mkachuee/Opportunistic","repo_kind":"official","path":"nhanes.py","file_url":"https://github.com/mkachuee/Opportunistic/blob/HEAD/nhanes.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e73a57669f9b7710"}},{"code_sha256_prefix":"6a0b7e2a1a279560","entry":"preprocess","repo":"mkachuee/Opportunistic","repo_kind":"official","path":"nhanes.py","file_url":"https://github.com/mkachuee/Opportunistic/blob/HEAD/nhanes.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6a0b7e2a1a279560"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}