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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2026 / Cilt: 9 Sayı: 2026

Unveiling Multiple Sclerosis Predictors: A Proposed Artificial Intelligence Approach Using Associative Classification

Multipl Skleroz Öngörücülerinin Açılması: İlişkisel Sınıflandırma Kullanarak Önerilen Yapay Zekâ Yaklaşımı

Ismail Onay(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, CERRAHPAŞA TIP FAKÜLTESİ, CERRAHPAŞA TIP PR.)0000-0001-6136-8309
Murat Kirisci(İSTANBUL ÜNİVERSİTESİ - CERRAHPAŞA, CERRAHPAŞA TIP FAKÜLTESİ)0000-0003-4938-5207
Cemil Çolak(İNÖNÜ ÜNİVERSİTESİ)0000-0001-5406-098X
Pages
1–10
Publication Date
2026-06-30
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AI Summary & Key Takeaways

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

Future research should incorporate more diverse datasets to validate these results further.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: Although multiple sclerosis (MS) is the leading non-traumatic cause of disability among young adults, the reasons behind its increasing prevalence remain elusive.
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Key Findings & Data:These individuals had sought care at the National Institute of Neurology and Neurosurgery (NINN) between 2006 and 2010.
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Conclusion & Impact:Future research should incorporate more diverse datasets to validate these results further.
🔬Design:Modeling & Simulation
📍Scope:2006 Verileri
⚙️Variables:Yapay zeka · İlişkisel Sınıflandırma · tanılama öngörücüler

Abstract

Objective: Although multiple sclerosis (MS) is the leading non-traumatic cause of disability among young adults, the reasons behind its increasing prevalence remain elusive. This study explores the use of artificial intelligence, specifically associative classification, to identify key predictors for the progression of multiple sclerosis (MS). Methods: The study utilized a publicly available dataset to forecast whether individuals have MS based on various personal traits. The relevant dataset originated from a cohort group study conducted on Mexican mestizo individuals recently identified with Clinically Isolated Syndrome (CIS). These individuals had sought care at the National Institute of Neurology and Neurosurgery (NINN) between 2006 and 2010. Associative rule mining was applied to uncover relationships between the variables and the development of Clinical Definite Multiple Sclerosis (CDMS). The effectiveness of the model was assessed using accuracy, balanced accuracy, sensitivity, specificity, positive & negative predictive values, and F1 measure with 95% confidence intervals. Results: Oligoclonal bands, breastfeeding history, education level, and specific MRI results were significant predictors of MS classification. Oligoclonal bands were particularly associated with a higher likelihood of CDMS. The proposed model performed with high accuracy (87.8%), sensitivity (89.6%), and specificity (86.3%), highlighting its effectiveness in predicting MS progression. Conclusions: Artificial intelligence, through associative classification, provides valuable insights into MS progression by identifying significant predictors. These findings can support early diagnosis and contribute to the development of personalized treatment strategies. Future research should incorporate more diverse datasets to validate these results further.

Özet

Amaç: Multipl skleroz (MS) genç yetişkinler arasında önde gelen travma olmayan sakatlık nedeni olmasına rağmen, artan prevalansının arkasındaki nedenler zordur. Bu çalışma, multipl sklerozun (MS) ilerlemesi için temel öngörücüleri tanımlamak için yapay zekanın, özellikle de ilişkilendirici sınıflandırmanın kullanımını araştırmayı amaçlamaktadır. Yöntemler: Çalışma, bireylerin çeşitli kişisel özelliklere dayalı MS olup olmadığını tahmin etmek için halka açık bir veri kümesi kullanılmıştır. İlgili veri kümesi, yakın zamanda klinik izole sendrom (CIS) ile tanımlanan Meksika mestizo bireyleri üzerinde yapılan bir kohort grubu çalışmasından kaynaklanmıştır. Bu bireyler 2006-2010 yılları arasında Ulusal Nöroloji ve Nöroşirürji Enstitüsü'nde (NINN) bakım aradılar. Değişkenler arasındaki ilişkileri ortaya çıkarmak ve klinik kesin multipl sklerozun (CDMS) gelişimini ortaya çıkarmak için ilişkisel kural madenciliği uygulanmıştır. Modellerin etkinliği doğruluk, dengeli doğruluk, duyarlılık, özgüllük, pozitif ve negatif prediktif değerler ve F1 ölçüsü kullanılarak değerlendirildi. Bulgular: Oligoklonal bantlar, emzirme geçmişi, eğitim seviyesi ve spesifik MRI sonuçları MS sınıflandırmasının önemli belirleyicileriydi. Oligoklonal bantlar özellikle CDMS'nin daha yüksek bir olasılığı ile ilişkiliydi. Önerilen model, yüksek doğruluk (%87,8), duyarlılık (%89,6) ve özgüllük (%86,3) ile gerçekleştirerek MS ilerlemesini tahmin etmede etkinliğini vurgulamaktadır. Sonuçlar: Yapay zeka, ilişkisel sınıflandırma yoluyla, önemli öngörücüler belirleyerek MS ilerlemesine değerli bilgiler sağlar. Bu bulgular erken tanıyı destekleyebilir ve kişiselleştirilmiş tedavi stratejilerinin geliştirilmesine katkıda bulunabilir. Gelecekteki araştırmalar, bu sonuçları daha da doğrulamak için daha çeşitli veri kümeleri içermelidir.

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Cite This Article

Onay, I., Kirisci, M., Çolak, C. (2026). Unveiling Multiple Sclerosis Predictors: A Proposed Artificial Intelligence Approach Using Associative Classification. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 9 Sayı: 2026, 1–10. https://doi.org/10.38016/jista.1708339

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