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Shapley-Additive-Explanations-Based Factor Analysis for Dengue Severity Prediction using Machine Learning.

Shihab Uddin Chowdhury1, Sanjana Sayeed1, Iktisad Rashid1

  • 1Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka 1212, Bangladesh.

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|September 22, 2022
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Summary
This summary is machine-generated.

This study used machine learning to predict Dengue Haemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) in Vietnam and Bangladesh. XGBoost and hierarchical clustering identified key patient attributes linked to severe dengue outcomes.

Keywords:
Dengue Haemorrhagic FeverDengue Shock SyndromeShapley Additive ExplanationXGBoostingclinical datadenguehierarchical clusteringsupervisedunsupervised

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Area of Science:

  • * Medical Informatics
  • * Epidemiology
  • * Machine Learning in Public Health

Background:

  • * Dengue is a significant mosquito-borne viral disease, particularly in tropical and subtropical regions like Southeast Asia.
  • * Dengue Haemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS) are severe forms, often fatal, and disproportionately affect young children.
  • * Nationwide dengue epidemics pose a substantial public health challenge, necessitating advanced analytical approaches for prediction and prevention.

Purpose of the Study:

  • * To apply machine learning techniques to analyze dengue patient data from Vietnam and Bangladesh.
  • * To identify predictive factors for Dengue Haemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS).
  • * To compare the effectiveness of supervised and unsupervised learning methods on structured and unstructured dengue datasets.

Main Methods:

  • * For the structured Vietnamese dataset (VDengu), supervised learning, specifically the XGBoost decision tree classifier, was employed for predictive analysis.
  • * Shapley Additive Explanation (SHAP) was utilized with the XGBoost model to determine attribute significance and visualize attribute ranges impacting DHF/DSS cases.
  • * For the unstructured Bangladeshi dataset (BDengue), unsupervised learning via hierarchical clustering was applied to analyze complete blood count data and identify clusters associated with severe dengue.

Main Results:

  • * The XGBoost model demonstrated high efficacy in predicting dengue severity in the structured Vietnamese dataset.
  • * SHAP analysis identified key patient attributes influencing DHF and DSS development, with dependence plots illustrating their impact ranges.
  • * Hierarchical clustering successfully grouped patients based on blood components, revealing attributes contributing to DHF/DSS in the Bangladeshi cohort.

Conclusions:

  • * Machine learning, including supervised (XGBoost) and unsupervised (hierarchical clustering) methods, is effective for analyzing dengue data from diverse sources.
  • * Identifying significant patient attributes through models like XGBoost and SHAP can aid in predicting and understanding severe dengue progression.
  • * Comparative analysis of structured and unstructured data using different ML approaches provides valuable insights for dengue risk assessment and management.