A Medical Decision Support System for Mortality Rate Prediction using Machine Learning Algorithms

This paper presents the development and implementation of a comprehensive System to Support Medical DecisionMaking to identify and predict mortality rates in healthcare institutions using advanced machine learning techniques. The system employs ECLAT algorithm and Apriori-based association learning to discover hidden relationships between hospital resources and patient mortality rates. Built using Microsoft Visual Studio and SQL Server, the application analyzes critical healthcare parameters including specialist availability, nursing staff, hospital infrastructure, and patient care resources to provide actionable insights for mortality reduction. Key features include real-time data processing, dynamic pattern recognition, unsupervised learning implementation, and comprehensive GUI-based visualization. Performance evaluation demonstrates significant improvements in prediction accuracy with ECLAT algorithm achieving execution times of 2450-2572 milliseconds for datasets ranging from 100 to 2000 records. The system successfully addresses the critical gap in automated mortality pattern analysis tools specifically designed for healthcare resource optimization.