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

Reviews in Medical Informatics (124) Decision Support for Health Professionals (1659) Machine Learning (2261) Infectious Diseases (non-STD/STI) (1763) Artificial Intelligence (2398) Computer-Aided Diagnosis (281) Use Cases in Human, Animal, and Zoonotic Communicable/Infectious Disease Surveillance (148)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67859, first published .
Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review

Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review

Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review

Authors of this article:

Suhila Sawesi1 Author Orcid Image ;   Arya Jadhav2 Author Orcid Image ;   Bushra Rashrash3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Mohamed A, Abdelrehim M, Al-Barazie R. Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data. Scientific Reports 2025;15(1) View
  2. Mohtasham F, Hashemi Nazari S, Pourhoseingholi M, Kavousi K, Zali M, Huk M. Hybrid feature-selection and diversity-guided stacking framework for interpretable ensemble learning: Application to COVID-19 mortality prediction. PLOS One 2026;21(4):e0341198 View
  3. Kokkaew E, Koedsin W, Yomsatiankul J, Yomsatieankul W, Jitpeera C, Sakchainanon W. Environmental drivers and machine learning forecasting of leptospirosis: a multi-province study in Thailand. International Journal of Environmental Health Research 2026:1 View