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Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67859, first published .
Digital brain hologram with DNA, viruses, microscope, and test tubes

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

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
  4. Sawesi S, Sabbineni H, Shagamreddy R, Rashrash B. Cybersecurity and Privacy Risks of Generative AI Mental-Health Chatbots: A Systematic Review and Regulatory Framework. Journal of Multidisciplinary Healthcare 2026;Volume 19:1 View
  5. Arivazhagan A, Rajendran V, Vinodkumar K, Vinodhkumar K, Premkumar D, Chawhan P, Lepcha N, Kumar B, Suresh K, Gulati B, Balamurugan V. Spatio-temporal ensemble machine learning forecasts of animal leptospirosis in enzootic Indian States using integrated environmental predictors. Veterinary Research Communications 2026;50(4) View