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Updated: May 23, 2026

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

Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning

Kanica Anand Arora¹, Anuj Sharma², Deepak Prashar^2,3

¹School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India. kanicaanand01@gmail.com.

Scientific Reports

|May 21, 2026

Summary

This study introduces a novel multi-agent reinforcement learning (MARL) framework for Hepatitis C virus (HCV) diagnosis, significantly reducing complexity and cost. The system identifies key biomarkers, achieving high accuracy with fewer features.

Keywords:

Biomarker identification Clinical decision support Feature selection Hepatitis C diagnosis Machine learning Multi-agent reinforcement learning

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Last Updated: May 23, 2026

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Hepatology

Background:

Hepatitis C virus (HCV) infection affects 50 million globally, leading to liver cirrhosis and cancer.
Current diagnostic methods for HCV are complex and costly, often lacking improved accuracy.
Optimizing diagnostic approaches is crucial for effective patient management and public health.

Purpose of the Study:

To develop and validate a novel multi-agent reinforcement learning (MARL) framework for intelligent feature selection in HCV diagnosis.
To identify minimal yet highly effective biomarker subsets for accurate HCV classification.
To reduce diagnostic complexity and healthcare costs associated with traditional diagnostic methods.

Main Methods:

A four-agent MARL system (Greedy Q-learning, Exploratory Q-Learning, Parsimonious DQN, Random) was developed for dynamic feature selection.
The framework was validated on a real-world dataset of 1,004 patients with 14 clinical features.
Selected features were evaluated using Logistic Regression, Decision Tree, Random Forest, and XGBoost classifiers.

Main Results:

The MARL framework identified compact feature subsets (3-4 features) achieving high diagnostic performance (accuracy: 0.98-0.99, F1-score: 0.98-0.99).
The Parsimonious Deep Q-Network (DQN) agent consistently outperformed other agents.
Serum Glutamic Oxaloacetic Transaminase (SGOT) was identified as a critical biomarker for HCV detection.

Conclusions:

Reinforcement learning-based adaptive feature selection significantly enhances HCV diagnostic efficiency.
The MARL framework offers a scalable, interpretable, and clinically viable solution for early HCV detection.
This approach has potential applications in various healthcare analytics domains beyond HCV diagnosis.