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Predictive analysis of pediatric gastroenteritis risk factors and seasonal variations using VGG Dense
P T Pranesh1, Carmelin Durai Singh1, Anand Sivanandam2
1Center for Global Health Research, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Insights
A new deep learning model, VDHNC, accurately predicts pediatric gastroenteritis, improving early detection and seasonal outbreak forecasting. This method enhances patient care and public health strategies by identifying at-risk children sooner.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Pediatric Infectious Diseases
Background:
- Pediatric gastroenteritis is a leading cause of childhood illness and mortality globally.
- Current diagnostic methods often miss crucial risk factors and seasonal patterns, leading to delayed treatment and increased hospitalizations.
Purpose of the Study:
- To develop an advanced deep learning model for enhanced prediction, classification, and seasonal trend analysis of pediatric gastroenteritis.
- To improve early detection and risk assessment for pediatric gastroenteritis cases.
Main Methods:
- A hybrid deep learning model, VDHNC, was created by combining VGG16 and DenseNet architectures.
- Clinical, demographic, and environmental data were utilized, with preprocessing including imputation, normalization, outlier management, and SMOTE for class balancing.
- Model performance was validated against baseline models (SVM, Random Forest, XGBoost) using statistical tests like ANOVA and t-tests.
Main Results:
- The VDHNC model achieved 97% accuracy, outperforming baseline models in precision, recall, and AUC-ROC score.
- The model successfully identified seasonal patterns in gastroenteritis, aiding in the prediction of future outbreaks.
- Statistical analysis confirmed VDHNC's superiority with a p-value < 0.05.
Conclusions:
- The VDHNC model offers a reliable tool for the early detection and risk assessment of pediatric gastroenteritis.
- Its robustness and interpretability support real-time public health decision-making and hospital resource planning.
Abstract:
Pediatric gastroenteritis is a major reason for sickness and death among children worldwide, especially in places where healthcare and clean sanitation are scarce. Conventional methods of diagnosis overlook possible risks and seasonal trends, which results in patients receiving treatment too late and more of them being hospitalized. The study sets out to create a new deep learning method that boosts the initial prediction, proper classification, and seasonal trends of pediatric gastroenteritis through the use of hybrid convolutions. The VDHNC model was formed by merging the strong feature learning of VGG16 with the efficient information sharing feature of DenseNet. To create the model, data about clinical, demographic, and environmental aspects of pediatric patients were used. The dataset was preprocessed by using imputation, normalization, managing outliers, and using SMOTE to balance classes. Further validation was performed by analyzing the model performance using one-way ANOVA and pairwise t-tests with several baselines such as SVM, Random Forest, and XGBoost. The VDHNC model was able to achieve a high accuracy of 97%, and was more precise, recalled more information, and reported a higher AUC-ROC score than any other model. The model was able to discover signs of seasonal gastroenteritis, which assisted in predicting future outbreaks. A statistical test proved that VDHNC was better than the other approaches with a p-value of less than 0.05. VDHNC proves reliable when it comes to early detection and assessment of risk in pediatric gastroenteritis cases. The solidness and ease of understanding in this model suggest it can be helpful for making real-time public health decisions and planning hospital resources.
