Predicting Leptospirosis Using Baseline Laboratory Tests and Geospatial Mapping of Acute Febrile Illness Cases Through Machine Learning-Based Algorithm.

Autor:	Sengupta M; Microbiology, All India Institute of Medical Sciences, Kalyani, IND., Kundu A; Microbiology, All India Institute of Medical Sciences, Kalyani, IND., Mandal S; General Internal Medicine, All India Institute of Medical Sciences, Kalyani, IND., Chatterjee SS; Microbiology, All India Institute of Medical Sciences, Kalyani, IND., Ghoshal U; Microbiology, All India Institute of Medical Sciences, Kalyani, IND., Banerjee S; Microbiology, All India Institute of Medical Sciences, Kalyani, IND., Mukhopadhyay K; Pharmacology, All India Institute of Medical Sciences, Kalyani, IND.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 Nov 15; Vol. 16 (11), pp. e73779. Date of Electronic Publication: 2024 Nov 15 (Print Publication: 2024).
DOI:	10.7759/cureus.73779
Abstrakt:	Introduction Leptospirosis is a zoonotic infection caused by Leptospira bacteria, which is reemerging in various regions and often poses a diagnostic challenge due to its nonspecific symptoms. While most infections are mild, severe cases occur in 5-10% of patients and are associated with high mortality, especially in areas with poor sanitation and urbanization. This study aims to investigate the association of specific parameters with leptospirosis diagnosis using a machine learning model and geographic mapping tools to identify spatial patterns and high-risk areas for the disease. Methods An observational retrospective study conducted at a tertiary care center analyzed patients clinically suspected of leptospirosis over the course of one year. The study utilized laboratory investigations, geographic mapping, and machine learning models to explore the association between various laboratory parameters and the predictive diagnosis of leptospirosis. Results The study, conducted over one year at All India Institute of Medical Sciences, Kalyani, India, included 325 patients, of whom 43 (13.2%) tested positive for leptospirosis by IgM ELISA. Geographic mapping revealed case clusters around nearby districts of West Bengal, with a few cases from Tripura and Bangladesh. The study found no significant association between individual laboratory parameters and leptospirosis diagnosis. However, machine learning models, particularly k-nearest neighbors (KNN), demonstrated moderate predictive accuracy (accuracy: 74%, area under the curve: 0.6). Conclusion Geographic mapping identified clusters of leptospirosis cases; however, no significant association was found between individual laboratory parameters and the disease diagnosis. Machine learning models, particularly KNN, demonstrated moderate predictive accuracy. The study also highlighted the overlapping clinical features of leptospirosis, dengue, and scrub typhus in West Bengal, although it noted the absence of detailed clinical data as a limitation. Competing Interests: Human subjects: Consent for treatment and open access publication was obtained or waived by all participants in this study. The Institutional Ethics Committee of All India Institute of Medical Sciences, Kalyanii (ECR/1686/Inst/WB/2022) issued approval IEC/AIIMS/Kalyani/certificate/2024/041. The ethics approval followed was in accordance with the ethical standards of the responsible committee on human experimentation with the Helsinki Declaration. Given the study's retrospective nature and the exclusive use of laboratory data, the ethics committee requested and granted a waiver of consent. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. (Copyright © 2024, Sengupta et al.)
Databáze:	MEDLINE
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