A Study of Early Prediction and Classification of Arthritis Disease using Soft Computing Techniques

Arthritis is the most familiar element of disability in the World. At a rate of 20 million people in the US are suffering from Arthritis. It characterizes around 200 rheumatic diseases an d conditions that influence joints. The tissues surround the joint, and other connective tissue. Early disease prediction and diagnosis of Arthritis is a significant problem in Medical Field. To provide better results, we propose a framework based on soft computing techniques. First, the Arthritis data set is pre-processed by Integer Scaling Normalization. It helps to avoid redundant data and improve the processing speed. From the pre-processed data particular features are extracted by utilizing Categorical Principle Component Analysis method. The feature extraction depends on range categorization. Based on the categorization features are extracted from the input data set and then classified. Classification is performed by utilizing Neutrosophic Cognitive Maps with Genetic Algorithm. This framework provides high accuracy. From the classified data set disease can be easily predicted also it provides the detailed information about the Arthritis type. So it will help for early prediction and diagnosis of Arthrit is disease.