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Related Experiment Video

Updated: Jul 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models.

Yongxian Fan¹, Xiqian Lu², Guicong Sun²

¹School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China. yongxian.fan@gmail.com.

BMC Bioinformatics

|September 6, 2023

Summary

This summary is machine-generated.

This study introduces an interpretable machine learning model for accurate Hepatitis C prediction. The explainable AI approach enhances clinical trust by clarifying decision-making processes for early diagnosis.

Keywords:

Hepatitis C Interpretable artificial intelligence LIME Machine learning SHAP

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A Competent Hepatocyte Model Examining Hepatitis B Virus Entry through Sodium Taurocholate Cotransporting Polypeptide as a Therapeutic Target

Published on: May 10, 2022

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Hepatology

Background:

Hepatitis C is a widespread liver disease requiring early diagnosis for effective treatment and prognosis.
Current computational models for Hepatitis C prediction often lack transparency, hindering clinical adoption.
There is a critical need for explainable medical decision systems in Hepatitis C diagnosis.

Purpose of the Study:

To develop and evaluate Machine Learning (ML) models for predicting Hepatitis C.
To enhance model transparency using explainable AI techniques.
To build clinician and patient trust through interpretable prediction processes.

Main Methods:

Evaluated black-box models: Random Forest (RF), Support Vector Machine (SVM), and AdaBoost.
Utilized Bayesian-optimized RF as the final classification algorithm.
Employed SHapley Additive exPlanations (SHAP) for global model interpretation and Local Interpretable Model-Agnostic Explanations with stability (LIME_stability) for local explanations.

Main Results:

The proposed interpretable Hepatitis C prediction model demonstrated superior performance compared to state-of-the-art methods.
Achieved excellent predictive accuracy through rigorous fivefold cross-validation and independent testing.
Successfully provided both global and local explanations for model predictions.

Conclusions:

The interpretable Hepatitis C prediction system offers significant advantages over existing methods.
The model achieves high predictive performance while maintaining excellent interpretability.
Enhanced understanding of model decisions facilitates clinical trust and adoption for Hepatitis C diagnosis.