This paper is in the following e-collection/theme issue:

Machine Learning (2029) Ontologies, Classifications, and Coding (318) Decision Support for Health Professionals (1508) Clinical Informatics (1291) Artificial Intelligence (2055) Secondary Use of Clinical Data for Research and Surveillance (465) Data Science (354) Coronary Heart Disease (107) Tools, Programs and Algorithms (368)

Published on in Vol 12 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52896, first published .
Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Unsupervised Feature Selection to Identify Important ICD-10 and ATC Codes for Machine Learning on a Cohort of Patients With Coronary Heart Disease: Retrospective Study

Authors of this article:

Peyman Ghasemi1, 2 Author Orcid Image ;   Joon Lee1, 3, 4, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations (2) Metrics

Journals

  1. Ghasemi P, Greenberg M, Southern D, Li B, White J, Lee J. Personalized decision making for coronary artery disease treatment using offline reinforcement learning. npj Digital Medicine 2025;8(1) View
  2. Ezenkwu C, Starkey A, Aziz A. ClusterSwarm: cluster-specific feature selection using binary particle swarm optimisation. Computing 2025;107(9) View
  3. Rao C, Wang J, Liu Y, Yuan J. Risk factor identification mechanism for coronary artery disease based on multiple cross-filtering and binary cuckoo search. Scientific Reports 2025;15(1) View
  4. Pratyusha Miriyala G, Sinha A. OAS-XGB: An OptiFlect Adaptive Search Optimization Framework Using XGBoost to Predict Length of Stay for CAD Patients. IEEE Access 2025;13:168246 View