Rare Diseases Severity Prediction System Using a Machine Learning-Based Technique

The development of predictive models for rare diseases using machine learning is an important area of research with the potential to improve healthcare outcomes. Rare diseases are often difficult to diagnose and treat due to limited data and resources, making them an ideal application for machine learning techniques. This research proposes the use of machine learning algorithms to develop predictive models for rare diseases using patient data such as medical records, genetic information, and environmental factors. The aim is to provide clinicians with a tool for early diagnosis and treatment of rare diseases, that leads to better patient outcomes and also helps to reduce healthcare costs. The accuracy and feasibility of these models will be evaluated using real-world patient data, contributing to the growing body of literature in healthcare by using machine learning. While challenges such as biased or inaccurate predictions must be acknowledged, we believe that machine learning use in rare disease diagnosis and treatment has the potential to significantly improve healthcare outcomes.