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Healthcare decision support in machine learning

  • Unique Paper ID: 180703
  • Volume: 12
  • Issue: 1
  • PageNo: 1932-1935
  • 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.
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  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 1932-1935

Healthcare decision support in machine learning

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