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Fethi Choubani

Biographic Data

ID10933480
NAMEFethi Choubani
GIVEN NAMESFethi
FAMILY NAMEChoubani
SIGNATURECHOUBANI F
AFFILIATIONSInnov'Com Laboratory at SUPCOM University of Carthage (Ucar) Ariana Tunisia
ORCID0000-0001-5052-0539
VERIFIEDNo
TOTAL WORKS1
TOTAL CITATIONS1
WORKS AS AUTHOR1
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2025
H-INDEX1
  • Improving Symptom‐Based Medical Diagnosis Using Ensemble Learning Approaches

    Open Access•Leila Aissaoui Ferhi, Manel Ben Amar et al.•ARTICLE•Systems Research and Behavioral…•2025•Cited by: 1•References: 28

    Symptoms‐based health checkers are emerging as digital tools in modern healthcare offering patients the ability to self‐assess their health status by inputting symptoms and receiving diagnostic suggestions. These systems rely on machine learning models to accurately predict medical conditions based on symptom data. In this study, we explore the effectiveness of various machine learning algorithms with a particular focus on ensemble learning metho…

  • Improving Symptom‐Based Medical Diagnosis Using Ensemble Learning Approaches

    Open Access•Leila Aissaoui Ferhi, Manel Ben Amar et al.•ARTICLE•Systems Research and Behavioral…•2025•Cited by: 1•References: 28

    Symptoms‐based health checkers are emerging as digital tools in modern healthcare offering patients the ability to self‐assess their health status by inputting symptoms and receiving diagnostic suggestions. These systems rely on machine learning models to accurately predict medical conditions based on symptom data. In this study, we explore the effectiveness of various machine learning algorithms with a particular focus on ensemble learning metho…

  • Improving Symptom‐Based Medical Diagnosis Using Ensemble Learning Approaches

    Open Access•Leila Aissaoui Ferhi, Manel Ben Amar et al.•ARTICLE•Systems Research and Behavioral…•2025•Cited by: 1•References: 28

    Symptoms‐based health checkers are emerging as digital tools in modern healthcare offering patients the ability to self‐assess their health status by inputting symptoms and receiving diagnostic suggestions. These systems rely on machine learning models to accurately predict medical conditions based on symptom data. In this study, we explore the effectiveness of various machine learning algorithms with a particular focus on ensemble learning metho…

Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Bootstrap aggregating (1 work) · Computer Science (1 work) · Data mining (1 work) · Decision tree (1 work) · Ensemble learning (1 work) · Logistic regression (1 work) · Machine learning (1 work) · Machine Learning in Healthcare (1 work)

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