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Bohm BC, Borges FEDM, Silva SCM, Soares AT, Ferreira DD, Belo VS, Lignon JS, Bruhn FRP. Utilization of machine learning for dengue case screening. BMC Public Health 2024; 24:1573. [PMID: 38862945 PMCID: PMC11167742 DOI: 10.1186/s12889-024-19083-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 06/07/2024] [Indexed: 06/13/2024] Open
Abstract
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.
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Affiliation(s)
- Bianca Conrad Bohm
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil.
| | | | - Suellen Caroline Matos Silva
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil
| | - Alessandra Talaska Soares
- Laboratory of Veterinary Epidemiology, Graduate Program in Microbiology and Parasitology, Federal University of Pelotas, Capão do Leão, Rio Grande do Sul, Brazil
| | | | - Vinícius Silva Belo
- Federal University of São, João del-Rei, Midwest Dona Lindu campus, Divinópolis, Minas Gerais, Brazil
| | - Julia Somavilla Lignon
- Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil
| | - Fábio Raphael Pascoti Bruhn
- Laboratory of Veterinary Epidemiology, Preventive Veterinary Department, Federal University of Pelotas,, Capão do Leão, Rio Grande do Sul, Brazil
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Chowdhury AH, Rad D, Rahman MS. Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree-based machine learning models. Health Sci Rep 2024; 7:e2037. [PMID: 38650723 PMCID: PMC11033350 DOI: 10.1002/hsr2.2037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/21/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Background and Aims Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students. Methods A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems. The tree-based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves. Results Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness-eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia. Conclusion The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.
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Affiliation(s)
| | - Dana Rad
- Center of Research Development and Innovation in PsychologyAurel Vlaicu University of AradAradRomania
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Kuo CY, Yang WW, Su ECY. Improving dengue fever predictions in Taiwan based on feature selection and random forests. BMC Infect Dis 2024; 24:334. [PMID: 38509486 PMCID: PMC10953060 DOI: 10.1186/s12879-024-09220-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 03/12/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan. RESULTS This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results. CONCLUSIONS Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.
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Affiliation(s)
- Chao-Yang Kuo
- Smart Healthcare Interdisciplinary College, National Taipei University of Nursing and Health Sciences, No.365, Mingde Road, Beitou District, Taipei City, 112303, Taiwan
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.301, Yuantong Road, Zhonghe District, New Taipei City, 23564, Taiwan
| | - Wei-Wen Yang
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.301, Yuantong Road, Zhonghe District, New Taipei City, 23564, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.301, Yuantong Road, Zhonghe District, New Taipei City, 23564, Taiwan.
- Clinical Big Data Research Center, Taipei Medical University Hospital, No.252 Wuxing Street, Xinyi District, Taipei City, 110, Taiwan.
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Zhang T, Rabhi F, Chen X, Paik HY, MacIntyre CR. A machine learning-based universal outbreak risk prediction tool. Comput Biol Med 2024; 169:107876. [PMID: 38176209 DOI: 10.1016/j.compbiomed.2023.107876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/12/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024]
Abstract
In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targeting only one specific disease and one country. Differences between countries and diseases (e.g., different economic conditions, different modes of transmission, etc.) pose challenges for building models with cross-country and cross-disease prediction capabilities. The limitation of universality affects domestic and international efforts to control and prevent pandemic outbreaks. To address this problem, we used outbreak data from 43 diseases in 206 countries to develop a universal risk prediction system that can be used across countries and diseases. This system used five machine learning models (including Neural Network XGBoost, Logistic Boost, Random Forest and Kernel SVM) to predict and vote together to make ensemble predictions. It can make predictions with around 80%-90 % accuracy from economic, cultural, social, and epidemiological factors. Three different datasets were designed to test the performance of ML models under different realistic situations. This prediction system has strong predictive ability, adaptability, and generality. It can give universal outbreak risk assessment that are not limited by border or disease type, facilitate rapid response to pandemic outbreaks, government decision-making and international cooperation.
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Affiliation(s)
- Tianyu Zhang
- FinanceIT Research Group, University of New South Wales, Sydney, NSW, Australia.
| | - Fethi Rabhi
- FinanceIT Research Group, University of New South Wales, Sydney, NSW, Australia
| | - Xin Chen
- Biosecurity Program, The Kirby Institute, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Hye-Young Paik
- School of Computer Science and Engineering, Faulty of Engineering, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Chandini Raina MacIntyre
- Biosecurity Program, The Kirby Institute, University of New South Wales, Sydney, NSW, 2052, Australia; College of Public Service & Community Solutions, Arizona State University, Tempe, AZ, 85004, United States
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Zargari Marandi R, Leung P, Sigera C, Murray DD, Weeratunga P, Fernando D, Rodrigo C, Rajapakse S, MacPherson CR. Development of a machine learning model for early prediction of plasma leakage in suspected dengue patients. PLoS Negl Trop Dis 2023; 17:e0010758. [PMID: 36913411 PMCID: PMC10035900 DOI: 10.1371/journal.pntd.0010758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 03/23/2023] [Accepted: 02/24/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND At least a third of dengue patients develop plasma leakage with increased risk of life-threatening complications. Predicting plasma leakage using laboratory parameters obtained in early infection as means of triaging patients for hospital admission is important for resource-limited settings. METHODS A Sri Lankan cohort including 4,768 instances of clinical data from N = 877 patients (60.3% patients with confirmed dengue infection) recorded in the first 96 hours of fever was considered. After excluding incomplete instances, the dataset was randomly split into a development and a test set with 374 (70%) and 172 (30%) patients, respectively. From the development set, five most informative features were selected using the minimum description length (MDL) algorithm. Random forest and light gradient boosting machine (LightGBM) were used to develop a classification model using the development set based on nested cross validation. An ensemble of the learners via average stacking was used as the final model to predict plasma leakage. RESULTS Lymphocyte count, haemoglobin, haematocrit, age, and aspartate aminotransferase were the most informative features to predict plasma leakage. The final model achieved the area under the receiver operating characteristics curve, AUC = 0.80 with positive predictive value, PPV = 76.9%, negative predictive value, NPV = 72.5%, specificity = 87.9%, and sensitivity = 54.8% on the test set. CONCLUSION The early predictors of plasma leakage identified in this study are similar to those identified in several prior studies that used non-machine learning based methods. However, our observations strengthen the evidence base for these predictors by showing their relevance even when individual data points, missing data and non-linear associations were considered. Testing the model on different populations using these low-cost observations would identify further strengths and limitations of the presented model.
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Affiliation(s)
- Ramtin Zargari Marandi
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Preston Leung
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | - Daniel Dawson Murray
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | | | | | - Chaturaka Rodrigo
- Viral Immunology Systems Program (VISP), Kirby Institute, UNSW Sydney, Sydney, Australia
| | | | - Cameron Ross MacPherson
- Centre of Excellence for Health, Immunity and Infections (CHIP), Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
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Early Warning of Infectious Diseases in Hospitals Based on Multi-Self-Regression Deep Neural Network. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8990907. [PMID: 36032546 PMCID: PMC9410942 DOI: 10.1155/2022/8990907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022]
Abstract
Objective. Infectious diseases usually spread rapidly. This study aims to develop a model that can provide fine-grained early warnings of infectious diseases using real hospital data combined with disease transmission characteristics, weather, and other multi-source data. Methods. Based on daily data reported for infectious diseases collected from several large general hospitals in China between 2012 and 2020, seven common infectious diseases in medical institutions were screened and a multi self-regression deep (MSRD) neural network was constructed. Using a recurrent neural network as the basic structure, the model can effectively model the epidemiological trend of infectious diseases by considering the current influencing conditions while taking into account the historical development characteristics in time-series data. The fitting and prediction accuracy of the model were evaluated using mean absolute error (MAE) and root mean squared error. Results. The proposed approach is significantly better than the existing infectious disease dynamics model, susceptible-exposed-infected-removed (SEIR), as it addresses the concerns of difficult-to-obtain quantitative data such as latent population, overfitting of long time series, and considering only a single series of the number of sick people without considering the epidemiological characteristics of infectious diseases. We also compare certain machine learning methods in this study. Experimental results demonstrate that the proposed approach achieves an MAE of 0.6928 and 1.3782 for hand, foot, and mouth disease and influenza, respectively. Conclusion. The MRSD-based infectious disease prediction model proposed in this paper can provide daily and instantaneous updates and accurate predictions for epidemic trends.
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Bhatia S, Bansal D, Patil S, Pandya S, Ilyas QM, Imran S. A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions. Front Public Health 2022; 10:884645. [PMID: 35712272 PMCID: PMC9197220 DOI: 10.3389/fpubh.2022.884645] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/26/2022] [Indexed: 11/30/2022] Open
Abstract
Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.
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Affiliation(s)
- Surbhi Bhatia
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Dhruvisha Bansal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Seema Patil
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Qazi Mudassar Ilyas
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Sajida Imran
- Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
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Baharom M, Ahmad N, Hod R, Abdul Manaf MR. Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review. Healthc Policy 2022; 15:871-886. [PMID: 35535237 PMCID: PMC9078425 DOI: 10.2147/rmhp.s361106] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/16/2022] [Indexed: 12/01/2022] Open
Abstract
Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.
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Affiliation(s)
- Mazni Baharom
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
| | - Norfazilah Ahmad
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
- Correspondence: Norfazilah Ahmad, Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia, Tel +60391458781, Fax +60391456670, Email
| | - Rozita Hod
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
| | - Mohd Rizal Abdul Manaf
- Department of Community Health, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala Lumpur, 56000, Malaysia
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