An ANOVA-SMOTE-Based Framework for Multi-Class Autoimmune Disease Classification

Abstract

Diagnosis of autoimmune disorders is prone to significant difficulties as the clinical picture is overlapped with complicated pathophysiology. The proposed study is the multi-class classification of autoimmune diseases based on a machine learning framework and taking into account four disease categories, namely, Graves’ Disease, Sjögren Syndrome, Rheumatoid Arthritis, and Systemic Lupus Erythematosus as well as a healthy control group, which leads to a five-class classification problem. The framework takes advantage of an integrated dataset, which includes demographic, hematological, biochemical, as well as immunological characteristics. K-Nearest Neighbors (KNN), Linear Support Vector Classifier (SVC), Decision Tree, Naive Bayes, Logistic Regression, and Ridge Classifier are six classification algorithms that were systematically tested under the same conditions of experiment. In order to improve the performance of the models and solve the problem of the class imbalance, an ANOVA-based feature selection strategy was utilized to be combined with the Synthetic Minority over Sampling Technique (SMOTE). The findings of experimental studies confirm that nonlinear classifiers, especially, Decision Tree and KNN, are much better than the linear models in terms of F1-score, recall, and precision. The added feature of the framework is the incorporation of a healthy control group, which enhances the capacity of the framework to differentiate between a diseased and a non-diseased person and increase the clinical applicability. In general, the suggested ANOVA-SMOTE-based framework can be considered as a powerful and scalable method of non-invasive autoimmune diseases classification, which can be implemented in the real clinical diagnostics and decision support.