Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network

Aug 5, 2026

Upload by Ezra

Periodic monitoring of Legionella in hospital water networks enables preventive actions that reduce the risk of legionellosis for patients and healthcare workers. The study standardizes a predictive approach for Legionella contamination risk by comparing machine learning with conventional and combined models using 2021–2022 water sampling, structural/environmental parameters, and model evaluation on held-out data. Results show higher accuracy and sensitivity for machine learning, informing future dataset expansion and validation.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Problem background and health impact

Water safety planning and regulatory context

Drivers of Legionella proliferation

Study design and methods

Sampling strategy and parameters collected

Model building and evaluation metrics

Results

Performance comparison across model types

Key influencing parameters

Conclusions

Ann Ig. 2025 Jan-Feb; 37(1): 128-140 doi: 10.7416/ai.2024.2644 . Epub 2024 Jul 11. Machine learning vs. regression models to predict the risk of Legionella contamination in a hospital water network Osvalda De Giglio 1 , Fabrizio Fasano 1 , Giusy Diella 1 , Valentina Spagnuolo 1,2 , Francesco Triggiano 1 , Marco Lopuzzo 1,2 , Francesca Apollonio 1 , Carla Maria Leone3 , Maria Teresa Montagna 1 Keywords: Legionella; Machine learning; Water network; Hospital; Artificial Intelligence Parole chiave: Legionella; Machine Learning; Rete idrica; Ospedale; Intelligenza artificiale Abstract Introduction. The periodic monitoring of Legionella in hospital water networks allows preventive measures to be taken to avoid the risk of legionellosis to patients and healthcare workers. Study design. The aim of the study is to standardize a method for predicting the risk of Legionella contamination in the water supply of a hospital facility, by comparing Machine Learning, conventional and combined models. Methods. During the period July 2021– October 2022, water sampling for Legionella detection was performed in the rooms of an Italian hospital pavilion (89.9% of the total number of rooms). Fifty-eight parameters regarding the structural and environmental characteristics of the water network were collected. Models were built on 70% of the dataset and tested on the remaining 30% to evaluate accuracy, sensitivity, and specificity. Results. A total of 1,053 water samples were analyzed and 57 (5.4%) were positive for Legionella. Of the Machine Learning models tested, the most efficient had an input layer (56 neurons), hidden layer (30 neurons), and output layer (two neurons). Accuracy was 93.4%, sensitivity was 43.8%, and specificity was 96%. The regression model had an accuracy of 82.9%, sensitivity of 20.3%, and specificity of 97.3%. The combination of the models achieved an accuracy of 82.3%, sensitivity of 22.4%, and specificity of 98.4%. The most important parameters that influenced the model results were the type of water network (hot/cold), the replacement of filter valves, and atmospheric temperature. Among the models tested, Machine Learning obtained the best results in terms of accuracy and sensitivity. Conclusions. Future studies are required to improve these predictive models by expanding the dataset using other parameters and other pavilions of the same hospital. 1 Interdisciplinary Department of Medicine, Hygiene Section, University of Bari Aldo Moro, Bari, Italy 2 Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J), University of Bari Aldo Moro, Bari, Italy 3 Azienda Ospedaliero Universitaria Policlinico di Bari, Hygiene Section, Bari, Italy Annali di Igiene : Medicina Preventiva e di Comunità (Ann Ig) ISSN 1120-9135 [https://www.annali-igiene.it](https://www.annali-igiene.it) Copyright © Società Editrice Universo (SEU), Roma, Italy Machine learning model to predict Legionella contamination 129 Introduction Legionella are Gram-negative bacteria that can colonize natural (e.g., rivers, lakes, and ponds) and artificial aquatic environments (e.g., drinking water systems, taps, faucets, showers, cooling towers, and fountains) (1) . After individuals inhale contaminated aerosols, they can develop various clinical forms of legionellosis, such as a flu-like illness (Pontiac fever) or severe pneumonia known as Legionnaires’disease (LD) (2) . The disease can be of community or nosocomial origin. In recent years, nosocomial legionellosis has attracted particular attention because of the complexity of hospital water systems and the vulnerability of hospitalized patients, which can lead to serious consequences with a high mortality rate (3) . The World Health Organization proposed the Water Safety Plan (WSP) in 2004 and revised it in subsequent years to both organize and systematize drinking water management practices and ensure the applicability of these practices to drinking water q

FAQs

What problem does the study address in hospital settings?

It addresses how to predict the risk of Legionella contamination in a hospital water network so preventive measures can be taken for patients and healthcare workers.

How were the predictive models evaluated?

Water sampling data collected between July 2021 and October 2022 were split into training (70%) and testing (30%) sets, and models were assessed using accuracy, sensitivity, and specificity.

Which modeling approach performed best and which factors influenced results most?

Machine learning achieved the best overall performance in terms of accuracy and sensitivity. The most influential parameters included the water network type (hot/cold), replacement of filter valves, and atmospheric temperature.

Aug 5, 2026

Upload by Ezra