Towards a Stacking Ensemble Model for Predicting Diabetes Mellitus using Combination of Machine Learning Techniques

Diabetes Mellitus (DM) is a chronic disease affecting the world's population, it causes long-term issues such as kidney failure, blindness, and heart disease, hurting one's quality of life. Diagnosing diabetes mellitus in an early stage is a challenge and a decisive decision for medical experts, as delay in diagnosis leads to complications in controlling the progression of the disease. Therefore, this research aims to develop a novel stacking ensemble model to predict diabetes mellitus a combination of machine learning models, where an ensemble of Prediction classifiers was used, such as Random Forest (RF), Logistic Regression (LR), as base learners' models, and the Extreme gradient Boosting model (XGBoost) as a Meta-Learner model. The results indicated that our proposed stacking model can predict diabetes mellitus with 83% accuracy on Pima dataset and 97% with DPD dataset. In conclusion, our proposed model can be used to build a diagnostic application for diabetes mellitus, as recommend testing our model on a huge and diverse dataset to obtain more accurate results.

@article{Alzubaidi2023,
  title     = {Towards a Stacking Ensemble Model for Predicting Diabetes Mellitus using Combination of Machine Learning Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Abdulaziz A Alzubaidi and Sami M Halawani and Mutasem Jarrah},
  doi       = {10.14569/IJACSA.2023.0141236},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141236}
}