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Journals / Experimed / 2026 / Volume: 16 Issue: 1

Machine Learning–Based Prediction of Autoimmune Diseases in Primary Care

Ahmet Emre Hatır(Uluırmak Family Health Center, Department of Family Medicine)0000-0001-8094-3444
Mustafa Onmaz(BANDIRMA ONYEDI EYLUL UNIVERSITY, SCHOOL OF MEDICINE)0000-0002-1962-9357
Journal
Experimed
Pages
—
Publication Date
2026-08-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

SHAP improves transparency; however, external and prospective validation is needed before clinical use.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: Autoimmune diseases are complex disorders with varied clinical manifestations, and their diagnosis often requires specialized tests not readily available in primary care.
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Key Findings & Data:Materials and Methods: A total of 1650 individuals from four family health centers were retrospectively evaluated.
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Conclusion & Impact:SHAP improves transparency; however, external and prospective validation is needed before clinical use.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence · autoimmune disease · diagnostic prediction

Abstract

Objective: Autoimmune diseases are complex disorders with varied clinical manifestations, and their diagnosis often requires specialized tests not readily available in primary care. This study aimed to design and assessed machine learning (ML) algorithms capable of identifying potential cases of autoimmune diseases by using routinely available, low-cost clinical and laboratory parameters. Materials and Methods: A total of 1650 individuals from four family health centers were retrospectively evaluated. Commonly used primary care tests—including complete blood count, biochemical markers, metabolic indices, and anthropometric variables—were incorporated. Five ML approaches (Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine, Logistic Regression, and Deep Learning) were implemented and compared. To improve interpretability, SHapley Additive Explanations (SHAP) were employed to determine the relative importance of predictors. Results: XGBoost demonstrated the highest performance (Accuracy: 0.77, AUC: 0.83, F1 score: 0.77), whereas Random Forest and Deep Learning achieved moderately high accuracy. SHAP interpretation revealed that sex, systolic blood pressure, mean platelet volume, monocyte levels, triglyceride-glucose, waist-to-height ratio, and neutrophil-tolymphocyte ratio were the most impactful predictors. Conclusion: ML models using routinely available measures may support risk stratification for autoimmune diseases in primary care. SHAP improves transparency; however, external and prospective validation is needed before clinical use.

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Cite This Article

Hatır, A. E., Onmaz, M. (2026). Machine Learning–Based Prediction of Autoimmune Diseases in Primary Care. Experimed, Volume: 16 Issue: 1. https://doi.org/10.26650/experimed.1786026

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