Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{180703,
        author = {Ankita Bhide and Ms. Rohini Tambe and Buddhabhushan Tikte and Hemant Gaikwad},
        title = {Healthcare decision support in machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1932-1935},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180703},
        abstract = {The integration of machine learning (ML) in 
healthcare decision support systems (DSS) enhances 
diagnostics and treatment recommendations. However, 
conventional ML models often lack interpretability and 
rely on centralized data, raising privacy concerns under 
HIPAA and GDPR. This paper proposes a privacy
preserving and interpretable ML architecture using 
federated learning (FL), differential privacy (DP), 
secure 
multiparty 
computation 
(SMC), and 
homomorphic encryption (HE). The approach enables 
collaborative training across distributed hospital 
systems without exposing patient data, while employing 
Random Forest for interpretable predictions with 
feature importance visualization. Natural Language 
Processing (NLP) enhances unstructured data analysis. 
Experiments on synthetic healthcare datasets 
demonstrate high accuracy, robust privacy, and 
interpretable outputs, offering a secure, scalable, and 
trustworthy AI solution for clinical decision-making.},
        keywords = {Interpretable Machine Learning, Healthcare  Decision Support, Random Forest, Feature Importance,  Natural Language Processing, Federated Learning,  Differential Privacy, Secure Multiparty Computation,  Homomorphic Encryption, HIPAA Compliance},
        month = {June},
        }