Skip to content
DraconiticResearch
Search Sources API
194,220 studies31 sourcesUpdated 33 min ago
Peer-reviewedCC BYDiagnostics (Basel, Switzerland)

Modeling Factors Associated with Smoking Dependence Severity Using Binary Classification-Based Machine Learning: Prediction with Hematological and Demographic Patient Data

  1. 1Department of Family Medicine, , Malatya 44210, Türkiye;
  2. 2Department of Industrial Engineering, , Gaziantep 27010, Türkiye;

Related

  1. Machine learning-based classification of renal cell carcinoma and upper tract urothelial carcinoma using preoperative hematological markers.
    Kanrapee Nuwatkrisin, Chanita Panwoon, Kiartisak Senchan +2
    Biomedical reportsPeer-reviewedCC BY-NC-NDFull textGeneral medicineEurope PMCPMC
  2. Construction of a risk prediction model for chemotherapy-induced taste alteration in patients with hematological malignancies
    ZHA Mengpei, CHEN Jing, WANG Aihong +3
    Zhongguo linchuang yanjiuPeer-reviewedCC BY-NC-NDFull textGeneral medicineDOAJ
  3. Gradient boosting models for gallstone disease classification: A comparative study using demographic and clinical data
    Kazi Sakib Hasan, Sumaiya Hossain Puspita
    PLOS OnePeer-reviewedCC BYFull textGeneral biologyGeneral mathematicsPLOSEurope PMCPMC
  4. Demographic Representation in Clinical Trials for Transthyretin-Associated Cardiac Amyloidosis: A Systematic Review
    Dimitri Ford, Chima Amadi, Akshay Chandora +6
    Reviews in cardiovascular medicinePeer-reviewedCC BYFull textCardiovascular medicineEurope PMC
  5. Latent profiles of physical and psychological symptoms in patients following rectal cancer surgery: Associated demographic and clinical characteristics
    Chen Hu, Yan Chen, Yinjin Li +7
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textOncologyCrossref
BibTeX
@article{korkmaz2026modeling,
  title = {{Modeling Factors Associated with Smoking Dependence Severity Using Binary Classification-Based Machine Learning: Prediction with Hematological and Demographic Patient Data}},
  author = {Yavuz Korkmaz and İbrahim Miraç Eligüzel and Adem Taha Özdemir},
  journal = {Diagnostics (Basel, Switzerland)},
  year = {2026},
  month = oct,
  url = {https://europepmc.org/article/PMC/PMC13702976},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Modeling Factors Associated with Smoking Dependence Severity Using Binary Classification-Based Machine Learning: Prediction with Hematological and Demographic Patient Data
AU  - Yavuz Korkmaz
AU  - İbrahim Miraç Eligüzel
AU  - Adem Taha Özdemir
PY  - 2026
DA  - 2026/10/01
JO  - Diagnostics (Basel, Switzerland)
UR  - https://europepmc.org/article/PMC/PMC13702976
SN  - 2075-4418
ER  -