Machine Learning and Prediction of Infectious Diseases: A Systematic Review

Santangelo, Omar Enzo; Gentile, Vito; Pizzo, Stefano; Giordano, Domiziana; Cedrone, Fabrizio

doi:10.3390/make5010013

Cited by 41 publications

(19 citation statements)

References 80 publications

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“…Santangelo and colleagues concluded that it is feasible to anticipate the emergence and progress of some infectious illnesses in this respect after conducting a comprehensive study with the goal of demonstrating whether it is possible to predict the spread of infectious diseases early using ML. Additionally, they said that correct and respectable outcomes may be obtained by integrating a variety of methods and ML algorithms 43 . Therefore, it would seem that, with additional development, the ML algorithm‐based prediction model might complement the present approaches for identifying high‐risk patients and grow to be a valuable tool for clinical staff in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Numerous research have employed ML approaches to forecast infectious outbreaks in recent years, with encouraging outcomes. Previous research findings have demonstrated that ML algorithms may predict infectious illness onset and spread with an accuracy that is on par with or better than that of classic statistical methods 43 . A thorough summary of meningitis diagnosis methods must be given due to the multitude of new procedures that are being developed.…”

Section: Introductionmentioning

confidence: 99%

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The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Ghaddaripouri,

Mousavi

et al. 2024

Health Science Reports

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Background and AimsThis systematic review aimed to evaluating the effectiveness of machine learning (ML) algorithms for the prediction and diagnosis of meningitis.MethodsOn November 12, 2022, a systematic review was carried out using a keyword search in the reliable scientific databases PubMed, EMBASE, Scopus, and Web of Science. The recommendations of Preferred Reporting for Systematic Reviews and Meta‐Analyses (PRISMA) were adhered to. Studies conducted in English that employed ML to predict and identify meningitis were deemed to match the inclusion criteria. The eligibility requirements were used to independently review the titles and abstracts. The whole text was then obtained and independently reviewed in accordance with the eligibility requirements.ResultsAfter all the research matched the inclusion criteria, a total of 16 studies were added to the systematic review. Studies on the application of ML algorithms in the three categories of disease diagnosis ability (8.16) and disease prediction ability (8.16) (including cases related to identifying patients (50%), risk of death in patients (25%), the consequences of the disease in childhood (12.5%), and its etiology [12.5%]) were placed. Among the ML algorithms used in this study, logistic regression (LR) (4.16, 25%) and multiple logistic regression (MLR) (4.16, 25%) were the most used. All the included studies indicated improvements in the processes of diagnosis, prediction, and disease outbreak with the help of ML algorithms.ConclusionThe results of the study showed that in all included studies, ML algorithms were an effective approach to facilitate diagnosis, predict consequences for risk classification, and improve resource utilization by predicting the volume of patients or services as well as discovering risk factors. The role of ML algorithms in improving disease diagnosis was more significant than disease prediction and prevalence. Meanwhile, the use of combined methods can optimize differential diagnoses and facilitate the decision‐making process for healthcare providers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Ghaddaripouri,

Mousavi

et al. 2024

Health Science Reports

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“…Machine learning algorithms can analyze large, complex data sets and identify patterns and trends that are difficult to be detected by humans. For this reason, an increasing number of studies in recent years have applied them to predict infectious disease outbreaks 21 . Random Forest 22 is one of the most commonly used and most powerful machine learning techniques or this purpose 21 .…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning with large-scale data for an effective vaccination against classical swine fever for wild boar in Japan

Ito,

Aguilar-Vega,

Bosch

et al. 2024

Sci Rep

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Classical swine fever has been spreading across the country since its re-emergence in Japan in 2018. Gifu Prefecture has been working diligently to control the disease through the oral vaccine dissemination targeting wild boars. Although vaccines were sprayed at 14,000 locations between 2019 and 2020, vaccine ingestion by wild boars was only confirmed at 30% of the locations. Here, we predicted the vaccine ingestion rate at each point by Random Forest modeling based on vaccine dissemination data and created prediction surfaces for the probability of vaccine ingestion by wild boar using spatial interpolation techniques. Consequently, the distance from the vaccination point to the water source was the most important variable, followed by elevation, season, road density, and slope. The area under the curve, model accuracy, sensitivity, and specificity for model evaluation were 0.760, 0.678, 0.661, and 0.685, respectively. Areas with high probability of wild boar vaccination were predicted in northern, eastern, and western part of Gifu. Leave-One-Out Cross Validation results showed that Kriging approach was more accurate than the Inverse distance weighting method. We emphasize that effective vaccination strategies based on epidemiological data are essential for disease control and that our proposed tool is also applicable for other wildlife diseases.

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“…Traditional approaches to disease surveillance and control often fall short due to their inherent limitations in handling the complexities of large-scale data, diverse variables, and non-linear relationships 4 . Herein lies the potential of machine learning to revolutionize disease prediction and management 5 .…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

Hussaini,

Tito,

Hasan

et al. 2023

Preprint

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Brucellosis, a zoonotic disease caused by Brucella bacteria, has emerged as a significant concern in Bangladesh, affecting both animals and humans, with economic repercussions and health risks. This paper addresses the urgent need for effective disease management in a country where agriculture and livestock are pivotal to the economy. The study focuses on identifying risk factors for brucellosis in dairy cattle and their correlation with factors collected from Central Cattle Breeding and Dairy Farm (CCBDF) and Military Dairy Farms (MDF) in Bangladesh. Serum and milk samples, along with demographic data, were collected and analyzed. To address the challenge of imbalanced data, the study introduces the application of the "SMOTE" Weka filter for the first time in Bangladesh's veterinary profession. This balances the dataset, enhancing the accuracy of predictive modeling. Machine learning models, specifically Multilayer Perceptron (MLP) and J48 decision tree algorithms, are employed for brucellosis prediction. MLP achieved a Correct Classification (CC) rate of 95.0801%, while J48 achieved 94.0503%, demonstrating their efficacy. Sensitivity analysis identifies key attributes, with retained placenta being the most significant, aiding in prioritizing control measures. Association Rules reveal patterns and relationships among disease-related factors, enhancing understanding and proactive prevention.

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Machine Learning and Prediction of Infectious Diseases: A Systematic Review

Cited by 41 publications

References 80 publications

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

The effect of machine learning algorithms in the prediction, and diagnosis of meningitis: A systematic review

Application of machine learning with large-scale data for an effective vaccination against classical swine fever for wild boar in Japan

Application of Machine Learning for Predicting Brucellosis Disease in Dairy Cattle

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