Enhancing Clinical Decision Support System : Risk Stratification of Hepatological Disorders by Advanced Machine Learning Models

Chandan Pan

doi:10.36948/ijfmr.2026.v08i02.73259

Enhancing Clinical Decision Support System : Risk Stratification of Hepatological Disorders by Advanced Machine Learning Models

Author(s)	Mr. Chandan Pan
Country	India
Abstract	Artificial Intelligence (AI) is reshaping healthcare services reflecting the increasing significance of data science for disease prediction. The integration of AI into diagnostic processes helps to optimize many processes in healthcare management. This research emphasis to enhance physician productivity in disease prediction, particularly concerning chronic hepatological disorders, to increase care management. It proposes a comprehensive framework for the incorporation of ensemble machine learning models into clinical practice. Furthermore, healthcare operations and strategic management data, informed by statistical analysis and AI can facilitate the identification of critical success factors for the healthcare management system through the proposed digital Healthcare Transformation. The methodology is driven by patient data derived taken from health systems at the University of California, Irvine (UCI), and encompasses the mapping of management diagnostics workflows. To refine the process of illness diagnosis, the proposed approach formulates a clinical decision-making algorithm leveraging contemporary AI technologies. The objectives of this study encompass mitigating physician burnout with streamlining care administration and augmenting diagnostic accuracy. This research highlights the importance of enhancing diagnostic accuracy and maximizing physician effectiveness for the treatment of liver diseases. Enhanced model performance efficiency, as indicated by KPIs is achieved through the implementation of a state of the art ensemble machine learning methodology. Our approach also emphasis on the comparative predictive strengths of individual Artificial Intelligence algorithms including Gradient Boosting (GB), Random Forest Classifier (RF) and Artificial Neural Network (ANN). To improve the accuracy and reliability of Hepatological Disorder predictions, the proposed Voting Ensemble Classifier integrates the outputs of these algorithms, thus addressing the limitations of using in a single model which may be vulnerable to data noise or overfitting.
Keywords	Advanced Machine Learning, Health Analytics, Clinical Decision Support , Ensemble Classifier
Field	Business Administration
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-01
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.73259

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Enhancing Clinical Decision Support System : Risk Stratification of Hepatological Disorders by Advanced Machine Learning Models

Share this