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Updated: May 11, 2025
Assays for the Specific Growth Rate and Cell-binding Ability of Rotavirus
Published on: January 28, 2019
A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute
Sourav Paul1, Minhazur Rahman2, Anutee Dolley3
1Department of Biotechnology, National Institute of Technology, Durgapur, West Bengal, India.
Insights
Machine learning models can predict rotavirus infection in children using clinical symptoms, aiding diagnosis in resource-limited settings. The Random Forest model showed the highest accuracy at 81.4%.
Area of Science:
- Pediatric infectious diseases
- Computational epidemiology
- Clinical informatics
Background:
- Rotavirus is a primary cause of severe dehydrating diarrhea in young children globally.
- Limited access to laboratory diagnostics in hospitals necessitates alternative diagnostic approaches.
- Machine learning (ML) shows promise for symptom-based disease diagnosis in resource-constrained environments.
Purpose of the Study:
- To develop an ML predictive model for rotavirus infection using clinical parameters.
- To avoid reliance on laboratory tests for diagnosis.
- To support timely and accessible rotavirus diagnosis.
Main Methods:
- Collected clinical data from 509 children, including symptoms like diarrhea, vomiting, fever, and dehydration.
- Performed correlation and feature selection (ANOVA F test) to identify important clinical indicators.
- Trained and compared seven supervised ML models: SVM, KNN, NB, Log_R, RF, DT, and XGBoost.
- Evaluated model performance using accuracy, precision, recall, specificity, F1, F2, macro F1, and AUC.
Main Results:
- The Random Forest (RF) model demonstrated superior performance among the seven ML models.
- RF achieved an accuracy of 81.4%, an F1 score of 86.9%, a macro F1-score of 77.3%, an F2 score of 86.5%, and an AUC of 89%.
Conclusions:
- ML models can aid in the symptom-based diagnosis of rotavirus-associated acute gastroenteritis in children.
- These models are particularly valuable in resource-limited settings.
- Further validation with larger datasets is recommended to optimize sensitivity and specificity for pediatric diarrheal diseases.
Background:
Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown promising potential in supporting symptom-based diagnosis of several diseases in resource-limited settings.
Objectives:
This study aims to develop a machine-learning predictive model integrated with multiple sources of clinical parameters specific to rotavirus infection without relying on laboratory tests.
Methods:
A clinical dataset of 509 children was collected in collaboration with the Regional Institute of Medical Sciences, Imphal, India. The clinical symptoms included diarrhea and its duration, number of stool episodes per day, fever, vomiting and its duration, number of vomiting episodes per day, temperature and dehydration. Correlation analysis is performed to check the feature-feature and feature-outcome collinearity. Feature selection using ANOVA F test is carried out to find the feature importance values and finally obtain the reduced feature subset. Seven supervised learning models were tested and compared viz., support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB), logistic regression (Log_R) , random forest (RF), decision tree (DT), and XGBoost (XGB). A comparison of the performances of the seven models using the classification results obtained. The performance of the models was evaluated based on accuracy, precision, recall, specificity, F1 score, macro F1, F2, and receiver operator characteristic curve.
Results:
The seven ML models were exhaustively experimented on our dataset and compared based on eight evaluation scores which are accuracy, precision, recall, specificity, F1 score, F2 score, macro F1 score, and AUC values computed. We observed that when the seven ML models were applied, RF performed the best with an accuracy of 81.4%, F1 score of 86.9%, macro F1-score of 77.3%, F2 score of 86.5% and area under the curve (AUC) of 89%.
Conclusions:
The machine learning models can contribute to predicting symptom-based diagnosis of rotavirus-associated acute gastroenteritis in children, especially in resource-limited settings. Further validation of the models using a large dataset is needed for predicting pediatric diarrheic populations with optimum sensitivity and specificity.
