[HTML][HTML] A novel machine learning approach for diagnosing diabetes with a self-explainable interface
This study introduces the first-ever self-explanatory interface for diagnosing diabetes
patients using machine learning. We propose four classification models (Decision Tree (DT),
K-nearest Neighbor (KNN), Support Vector Classification (SVC), and Extreme Gradient
Boosting (XGB)) based on the publicly available diabetes dataset. To elucidate the inner
workings of these models, we employed the machine learning interpretation method known
as Shapley Additive Explanations (SHAP). All the models exhibited commendable accuracy …
patients using machine learning. We propose four classification models (Decision Tree (DT),
K-nearest Neighbor (KNN), Support Vector Classification (SVC), and Extreme Gradient
Boosting (XGB)) based on the publicly available diabetes dataset. To elucidate the inner
workings of these models, we employed the machine learning interpretation method known
as Shapley Additive Explanations (SHAP). All the models exhibited commendable accuracy …
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