Machine Learning and SHAP Interpretability for Chronic Disease Understanding

Authors

Nnaemeka Charles Igwe, Dakota State University

Files

Description

Our study predicts non-communicable diseases (NCDs), such as diabetes, by leveraging machine learning algorithms. We leverage hyperparameter tuning techniques for model development and SHapley Additive exPlanation (SHAP) for results interpretations.

Publication Date

2025

Comments

Presented at 2025 SDSU Data Science Symposium.

Recommended Citation

Igwe, Nnaemeka Charles, "Machine Learning and SHAP Interpretability for Chronic Disease Understanding" (2025). External Research Posters. 15.
https://scholar.dsu.edu/erposters/15