A Wegner's Granulomatosis Risk Prediction Model Based on Machine Learning Algorithms

Jaleh Shoshtarian Malak; Samira Alsaeidi; Fatemeh Haji Ali Asgari; Fahimeh Khedmatkon

doi:10.18502/jbe.v9i2.14619

Jaleh Shoshtarian Malak Department of Electronic Health, Virtual School, Tehran University of Medical Science, Tehran, Iran.
Samira Alsaeidi Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
Fatemeh Haji Ali Asgari Department of Electronic Health, Virtual School, Tehran University of Medical Science, Tehran, Iran.
Fahimeh Khedmatkon Department of Electronic Health, Virtual School, Tehran University of Medical Science, Tehran, Iran.

DOI: https://doi.org/10.18502/jbe.v9i2.14619

Keywords: Wegener's granulomatosis relapse; Relapse prediction; Machine learning; Clinical decision-making; Xgboost algorithm; Birmingham vasculitis activity score; Predictive modeling; Healthcare analytics; Autoimmune diseases; Precision medicine

Abstract

Introduction: Prediction of Wegener's granulomatosis diagnosis and relapse is a complex process. In this study, we applied machine learning algorithms to predict Wegener's granulomatosis relapse.

Methods: In this research, 189 patients admitted to Amiralam Hospital were studied and followed for approximately 2 years. Patient features included demographics, organ involvement, symptoms, and other clinical data. Different popular machine learning algorithms were applied for predicting Wegener's granulomatosis relapse, including Support Vector Machines, Random Forest, Gradient Boosting, and XGBoost algorithms. The prediction model performance was measured for the different candidate prediction algorithms using accuracy, precision, recall, and F1-measure. The selected prediction model performance was calculated based on different relapse rates and major relapse occurrence according to Birmingham Vasculitis Activity Score (BVAS) fields.

Results: Applying different machine learning algorithms, the XGBoost algorithm performed the best. The results indicated that the prediction model's performance increased when calculating higher relapse rate possibilities. The XGBoost model had 82% accuracy while predicting more than one relapse rate and 92% accuracy in predicting more than twice the relapse rate. We also calculated the SHAP value for the prediction model. The results indicated that Cr, BVAS, lymphocyte percentage, vitamin D, nose involvement, alkaline phosphatase, diagnosis age, white blood cell count, erythrocyte sedimentation rate, and initial nose presentation are the 10 most important features according to SHAP value.

Conclusion: In this study, we have developed Wegener's granulomatosis relapse prediction model using machine learning algorithms. We achieved reasonable precision and recall for early prediction and decision- making regarding Wegener's granulomatosis relapse.