Predictive Modeling of Non-Communicable Diseases Using Social Determinants of Health as Features: A Review of Existing Approaches

Abstract

This paper presents a comprehensive review of the current state of predictive models for non-communicable disease (NCD) prevalence, focusing specifically on the use of social determinants of health (SDHs) as features for model training. The review's search strategy employed a thorough screening process to select sixteen studies for inclusion. These studies used supervised, unsupervised, and other algorithms to forecast NCD burden; the most frequently applied attributes were age, gender, fasting blood sugar (FBS), physical inactivity, obesity, and smoking. Evaluation methods included Percent Accuracy, Receiver Operating Characteristic (ROC), and Hamming loss. The review concludes that predictive models can accurately forecast NCD prevalence and highlights the need for further research incorporating SDH-related factors as features for model training.

Keywords: non-communicable diseases, social determinants of health, prediction, machine learning

References

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@article{2023_Bhoothookngoen_Sanchan,
  title   = {Predictive Modeling of Non-Communicable Diseases Using Social Determinants of Health as Features: A Review of Existing Approaches},
  author  = {Bhoothookngoen, Peatiphat and Sanchan, Nattapong},
  journal = {Srinakharinwirot University Engineering Journal},
  volume  = {19},
  number  = {1},
  pages   = {79--88},
  year    = {2023},
  month   = {Dec},
  address = {Bangkok, Thailand}
}

Bhoothookngoen, P., & Sanchan, N. (2023). Predictive Modeling of Non-Communicable Diseases Using Social Determinants of Health as Features: A Review of Existing Approaches. Srinakharinwirot University Engineering Journal, 19(1), 79–88.

More information

This paper is a continuing work in the Noncommunicable Disease Prediction project:

1. Predictive Modeling of Non-Communicable Diseases Using Social Determinants of Health as Features: A Review of Existing Approaches
2. Prevalence of Noncommunicable Diseases and Social Determinants of Health in Thailand: Insights from Public Datasets
3. Forecasting Noncommunicable Diseases in Thailand: Evaluating the Predictive Power of Social Determinants of Health-Related Features