A novel machine learning approach for diagnosing diabetes with a self-explainable interface

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 publicl...

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Bibliographic Details
Main Authors: Gangani Dharmarathne, Thilini N. Jayasinghe, Madhusha Bogahawaththa, D.P.P. Meddage, Upaka Rathnayake
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Healthcare Analytics
Subjects:
Machine learning
Diabetes
Predictive analytics
Self-explainable interface
Healthcare
Diagnosis
Online Access:http://www.sciencedirect.com/science/article/pii/S2772442524000030

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http://www.sciencedirect.com/science/article/pii/S2772442524000030

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