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

Abstract

Background: Leptospirosis, a zoonotic disease caused by Leptospira bacteria, continues to pose significant public health risks, particularly in tropical and subtropical regions.

Objective: This systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics.

Methods: Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches.

Results: Out of a total of 374 articles screened, 17 studies were included in the qualitative synthesis, representing approximately 4.5% of the initial pool. The review identified frequent use of algorithms such as support vector machines, artificial neural networks, decision trees, and convolutional neural networks (CNNs). Among the included studies, 88% (15/17) used traditional ML methods, and 24% (4/17) used DL techniques. Several models demonstrated high predictive performance, with reported accuracy rates ranging from 80% to 98%, notably with the U-Net CNN achieving 98.02% accuracy. However, public datasets were underused, with only 35% (6/17) of studies incorporating publicly available data sources; the majority (65%, 11/17) relied primarily on private datasets from hospitals, clinical records, or regional surveillance systems.

Conclusions: ML and DL techniques demonstrate potential for improving leptospirosis prediction and diagnosis, but future research should focus on using larger, more diverse datasets, adopting transfer learning strategies, and integrating advanced ensemble and validation techniques to strengthen model accuracy and generalization.

Keywords: artificial intelligence; convolutional neural networks; deep learning; diagnosis; leptospirosis; machine learning; prediction models; support vector machines; transfer learning; zoonotic diseases.

Figure 1.. Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram illustrating the search and selection process used to identify relevant studies. AI: artificial intelligence; ML: machine learning; DL: deep learning.

Figure 2.. Distribution of studies on machine learning and deep learning applications for leptospirosis diagnosis and prediction by year and task type.

Figure 3.. Distribution of risk of bias across domains in machine learning and deep learning studies for leptospirosis.

Figure 4.. Heatmap of classifier usage across included studies [1020-35undefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefinedundefined]. ANN: artificial neural network; DL: deep learning; FAM: fuzzy adaptive resonance theory mapping; FFN: feed forward network; GA: genetic algorithm; GWR: geographically weighted regression; J48: J48 decision tree; JRIP: repeated incremental pruning to produce error reduction; LASSO: least absolute shrinkage and selection operator regression; Maxent: maximum entropy model; ML: machine learning; RF: random forest; SVM: support vector machine; SVR: support vector regression; TAN: tree augmented naïve network; U-Net: U-Net convolutional neural network.

Figure 5.. Number of algorithm performance metrics used in reviewed articles of dataset types used (public and private). AUC: area under the curve; F1: F₁-score; MAE: mean absolute error; MSE: mean squared error; RMSE: root mean squared error.