Design a Linear Classification model with Support Vector Machine Algorithm on Autoimmune Disease data

Abstract

Machine Learning algorithms are profusely demonstrated in innumerable medical applications for prediction, diagnosis, and recommendations to treat patients in surgical and non-surgical departments. Support Vector Machine algorithm used for regression and classification problems. Support Vector Machines are used mostly for classification tasks due to their high efficiency in data stratification tasks. Support Vector Machine exceptionally used Machine Learning technique, in numerous fields as the outcomes are classified and predicted with a higher accuracy rate and with minimal computational resources. Due to the recent developments in Support Vector Machines such as kernel tricks, the performance of algorithms proves to be better than other algorithms. Autoimmune diseases are serious medical conditions to be diagnosed and treated on time to reduce the adverse effects of the disease. Early diagnosis of these diseases provides the scope for reversing the disease, improving the quality of life, and protecting from further damage to the organs and co-morbidities. Autoimmune disease cases have grown rapidly in the last 2 decades due to the adaptation of modern lifestyles in many countries across the globe. There is enormous research required in Autoimmune diseases using Computer diagnostic methods to diagnose the disease swiftly to initiate the appropriate treatment on time. Support Vector Machine algorithms efficiently provide the predictions as part of disease diagnosis and recommendation systems. This paper is intended to create an algorithm based on Linear classification predictions using Support Vector Machines on autoimmune disease data. WEKA open-source tool used for this model creation with Support Vector Machine Algorithm on the datasets downloaded from Centers for Disease Control and Prevention (National Center for Chronic Disease Prevention and Health Promotion).

Topics & Concepts