Implementation of Decision Tree Algorithm for Prediction of Rheumatoid Arthritis Disease

Abstract

A very challenging and difficult part of finding a cure for Rheumatoid Arthritis ( RA) disease is the same as finding a cure for an autoimmune disease. In the case of autoimmune diseases, it is very difficult to find the cause. RA is also one of the autoimmune diseases that causes inflammation in joints. It does not only limit to joints but also can spread or occur in various parts of the body like skin, lungs, and many others. Many features like age, sex, and previous history of certain diseases affect the occurrence of RA. The proposed ML model uses a decision tree algorithm to analyze a comprehensive set of clinical and laboratory variables collected from RA patients. These variables include age, ID, time, treatment, gender, and other baseline features. The ML model employs feature selection techniques to identify the most relevant predictors that contribute to disease progression. By training the decision tree algorithm on a large dataset of RA patients, the model generates an accurate predictive model for assessing disease severity and progression. The model is able to detect RA disease with almost 80% accuracy with the given training dataset. RA can have many other symptoms and characteristics. This data was trained on a basic dataset available from the Kaggle website. The dataset is in CSV format and has 6 features. The model was trained using a decision tree algorithm, which categorizes the data into different categories and then checks for specific data when needed in Jupyter Notebook. The study explored detecting basic symptoms and characteristics of RA disease. The Decision tree model has given accuracy of 86.5% and 74.4% on training and testing respectively. So Accuracy of the model is pretty good for the dataset used.