ArticleJournal of imaging2022
Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Association of Estimated Pulse Wave Velocity with Chronic Kidney Disease Risk: A Machine Learning Analysis Based on NHANES and CHARLS.Healthcare (Basel, Switzerland) · 2026Article
- A multimodal MRI radiomics model for distinguishing borderline from malignant ovarian epithelial tumors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Integrating intratumoral and peritumoral ultrasound radiomics with clinicopathological features to predict axillary lymph node metastasis in early breast cancer.Gland surgery · 2026Article
- CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.Chinese medical journal · 2026Article
- Development of a machine-learning based diagnosis procedure to distinguish aortic dissection from non-ST-elevation myocardial infarction.BMC medical informatics and decision making · 2026Article
- Machine learning-based prediction of rotator cuff tears using anatomical parameters: a retrospective cohort study.BMC musculoskeletal disorders · 2026Article
- Evaluation of an interpretable deep-learning model for the automated plan review of intensity-modulated radiation therapy.Frontiers in oncology · 2026Article
- Machine learning-based mortality prediction models for emergency department patients: a comparative analysis.Frontiers in medicine · 2026Article
- Study of a Kawasaki disease diagnostic prediction model based on the LightGBM machine learning algorithm.Frontiers in artificial intelligence · 2026Article
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- Application of Cluster Analysis Based on SHAP Values in Hemodialysis Patients Using Arteriovenous Fistula.International journal of general medicine · 2025Article
- Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus.Frontiers in pediatrics · 2025Article
- Machine learning-based prediction of disease-free survival in breast cancer patients with non-pathological complete response after neoadjuvant chemotherapy: a retrospective multicenter cohort study.American journal of cancer research · 2025Article
- Development and Validation of an Interpretable ML Model for Survival Prediction in Unresectable ESCC with Immunochemotherapy.Cancer management and research · 2025Article
- Early mortality prediction after severe trauma using ensemble machine learning: a single-center retrospective study.Frontiers in public health · 2025Article
- Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where most cases occur, and a large portion of them are detected among children under the age of ten, with severe conditions often progressing to a critical state known as Dengue Shock Syndrome (DSS). In this study, we analysed two separate datasets from two different countries- Vietnam and Bangladesh, which we referred as VDengu and BDengue, respectively. For the VDengu dataset, as it was structured, supervised learning models were effective for predictive analysis, among which, the decision tree classifier XGBoost in particular produced the best outcome. Furthermore, Shapley Additive Explanation (SHAP) was used over the XGBoost model to assess the significance of individual attributes of the dataset. Among the significant attributes, we applied the SHAP dependence plot to identify the range for each attribute against the number of DHF or DSS cases. In parallel, the dataset from Bangladesh was unstructured; therefore, we applied an unsupervised learning technique, i.e., hierarchical clustering, to find clusters of vital blood components of the patients according to their complete blood count reports. The clusters were further analysed to find the attributes in the dataset that led to DSS or DHF.
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