Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques

Abstract

Clinical Decision Support Systems (CDSS) play a crucial role in improving healthcare outcomes by assisting clinicians in diagnosis and treatment planning. This paper presents the design and implementation of an intelligent CDSS using machine learning techniques to enhance clinical decision-making. The proposed system integrates patient data, predictive modeling, and real-time analytics to support early disease detection and risk assessment. Various machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated for performance. The system architecture, data preprocessing methods, and evaluation metrics are discussed in detail. Experimental results demonstrate improved accuracy and efficiency compared to traditional rule-based systems. The study highlights the potential of AI-driven CDSS in modern healthcare environments.

Ähnliche Arbeiten