Multidisease Prediction and Causality Analysis with Hospital Management System

In healthcare domain, the demand for efficient diagnosis and hospital management systems has increased unprecedentedly. This research study proposes a novel Symptom Checker integrated with a Multidisease Hospital Management System, leveraging state-of-the-art Machine Learning (ML) algorithms such as Random Forest Classifier and XGB Regression. Unlike existing systems, this advanced framework outperforms conventional methods by offering increased accuracy and agility in diagnosing a multitude of medical conditions. The proposed system operates as a comprehensive tool, integrating patient symptoms with ML algorithms to accurately identify the diseases. Through the analysis of symptoms given as input by patients, the Random Forest Classifier preforms well in classifying a broad spectrum of diseases, demonstrating high precision in analysing complex symptom patterns. Furthermore, the XGB Regression model enhances the system’s predictive capabilities, enabling the estimation of disease progression and facilitating proactive treatment strategies. Moreover, the proposed approach extends beyond symptom analysis. It encompasses a robust hospital management component that streamlines administrative tasks, optimizes resource allocation, and enhances patient care.