Advancements In Disease Prediction And Diagnosis: Leveraging Linear Regression And Pattern Matching In Medical Informatics

Abstract

A notable development in medical informatics is the creation of disease prediction systems that combine pattern matching with linear regression. This method offers a comprehensive framework for early illness detection and prognosis. It is broken down into several modules, including knowledge discovery in medical systems, symptom identification (e.g., headache, fever, body pain, backache, swelling of joints, and running nose), pattern matching, and differential diagnosis. Healthcare workers can anticipate and diagnose diseases more effectively by using this methodology, which makes use of past data to find patterns within symptom presentations. This strategy has the potential to transform illness care and enhance patient outcomes by combining a thorough understanding of medical symptoms and their presentations with machine learning techniques like linear regression.