An Adaptive Soft-Selection Stacking-Based Model for Chronic Disease Prediction

This publication has 22 references indexed in Scilit:

• Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
  The Lancet, 2024
• Global burden associated with 85 pathogens in 2019: a systematic analysis for the Global Burden of Disease Study 2019
  The Lancet Infectious Diseases, 2024
• Learning from Imbalanced Data in Healthcare: State-of-the-Art and Research Challenges
  2024
• Enhanced cardiovascular disease prediction model using random forest algorithm
  Informatics in Medicine Unlocked, 2023
• Detection of the chronic kidney disease using XGBoost classifier and explaining the influence of the attributes on the model using SHAP
  Scientific Reports, 2023
• A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease
  Decision Analytics Journal, 2023
• Approximating XGBoost with an interpretable decision tree
  Information Sciences, 2021
• Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis
  International Journal of Medical Informatics, 2021
• The practical implementation of artificial intelligence technologies in medicine
  Nature Medicine, 2019
• Application of classification techniques on development an early-warning system for chronic illnesses
  Expert Systems with Applications, 2012