Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes...

Full description

Saved in:

Bibliographic Details
Published in:	Proceedings (IEEE Region 10 Symposium. Online) pp. 1 - 6
Main Authors:	MB, Abhijna, P, Akul, Kodipalli, Ashwini
Format:	Conference Proceeding
Language:	English
Published:	IEEE 27-09-2024
Subjects:	Accuracy Boosting Brain modeling Decision Tree Explainable AI K-Nearest Neighbors (KNN) LIME and SHAP Logistic regression Machine learning Medical treatment Nearest neighbor methods Planning Random forests Regression tree analysis SVM XAI
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence.
ISSN:	2642-6102
DOI:	10.1109/TENSYMP61132.2024.10752211