Interpretable Decision Tree Model for Classification of Acute Appendicitis Using Clinical, Laboratory, and Imaging Data: A Retrospective Single-Center Study.

Öz

Abstract Background: Acute appendicitis remains a common surgical emergency, and diagnostic uncertainty persists despite the use of clinical scores and imaging. We examined whether an interpretable decision tree could support classification of appendicitis using routinely available clinical, laboratory, and imaging variables. Methods: This retrospective single-center study included 281 patients who presented to El-Youssef Hospital with suspected appendicitis between June 2015 and May 2020. A J48 decision tree was developed in WEKA using age, fever, white blood cell count, neutrophil percentage, C-reactive protein, ultrasound findings, and computed tomography findings. Model performance was estimated with 10-fold cross-validation and reported using the confusion matrix. Results: The decision tree achieved an overall accuracy of 84.3%, with a sensitivity of 89.8%, a specificity of 76.3%, a positive predictive value of 84.8%, and a negative predictive value of 83.7%. Ultrasound, computed tomography, white blood cell count, and neutrophil percentage were the most informative predictors. Misclassifications were most common in patients with atypical presentations or borderline laboratory values. Conclusion: In this single-center retrospective cohort, a decision tree classifier showed good internal performance for distinguishing appendicitis from non-appendicitis. The findings support the feasibility of an interpretable decision-support model, but they do not establish clinical effectiveness. External multicenter validation and comparison with alternative algorithms and existing scoring systems are needed before clinical implementation. Keywords: Acute appendicitis; data mining; decision tree; machine learning; diagnostic classification; clinical decision support.

Anahtar Kelimeler

• Acute appendicitis
• data mining
• decision tree
• machine learning
• diagnostic classification
• clinical decision support

Destekleyen Kurum

None

Proje Numarası

12022015

Etik Beyan

All procedures were conducted in accordance with applicable laws, institutional regulations, and ethical standards. This research was reviewed and approved by the Scientific Committee of the Lebanese University, Faculty of Economics and Business Administration, 3rd Branch, Department of Computer Science (North Lebanon), in February 2015. The Committee determined that this study met the criteria for exemption from individual patient consent requirements. As this was a double-blinded, retrospective study based on a mathematical approach examining the provisional role of data mining in healthcare, and as all data were anonymized prior to analysis, the Ethics Committee of the Lebanese University determined that individual informed consent was not required. Hospital administrative approval was obtained prior to data collection. We declare that patient anonymity was strictly maintained throughout all stages of the study. All necessary measures were implemented to ensure the confidentiality and security of medical records, clinical data, and laboratory and radiological findings.

Klinik, Laboratuvar ve Görüntüleme Verileri Kullanılarak Akut Apandisitin Sınıflandırılmasına Yönelik Yorumlanabilir Karar Ağacı Modeli: Retrospektif Tek Merkezli Bir Çalışma.

Öz

Özet Amaç: Akut apandisit, yaygın görülen cerrahi acillerden biri olmaya devam etmekte olup, klinik skorlama sistemleri ve görüntüleme yöntemlerinin kullanımına rağmen tanısal belirsizlik sürmektedir. Bu çalışmada, rutin olarak elde edilebilen klinik, laboratuvar ve görüntüleme değişkenleri kullanılarak yorumlanabilir bir karar ağacı modelinin apandisit sınıflandırmasını destekleyip destekleyemeyeceği araştırıldı. Yöntemler: Bu retrospektif tek merkezli çalışmaya, Haziran 2015 ile Mayıs 2020 tarihleri arasında El-Youssef Hastanesi’ne şüpheli apandisit tanısıyla başvuran 281 hasta dahil edildi. WEKA yazılımı kullanılarak yaş, ateş, beyaz küre sayısı, nötrofil yüzdesi, C-reaktif protein düzeyi, ultrasonografi bulguları ve bilgisayarlı tomografi bulgularını içeren bir J48 karar ağacı modeli geliştirildi. Model performansı 10 katlı çapraz doğrulama yöntemiyle değerlendirildi ve karışıklık matrisi kullanılarak raporlandı. Bulgular: Karar ağacı modeli %84,3 genel doğruluk oranı elde etti. Modelin duyarlılığı %89,8, özgüllüğü %76,3, pozitif prediktif değeri %84,8 ve negatif prediktif değeri %83,7 olarak bulundu. Ultrasonografi, bilgisayarlı tomografi, beyaz küre sayısı ve nötrofil yüzdesi en bilgilendirici belirteçler olarak saptandı. Yanlış sınıflandırmalar en sık atipik klinik prezentasyona veya sınırda laboratuvar değerlerine sahip hastalarda görüldü. Sonuç: Bu retrospektif tek merkezli kohortta, karar ağacı sınıflandırıcısı apandisit ve non-apandisit olgularını ayırt etmede iyi düzeyde içsel performans göstermiştir. Bulgular, yorumlanabilir bir klinik karar destek modelinin uygulanabilirliğini desteklemekle birlikte, klinik etkinliğini kanıtlamamaktadır. Klinik uygulamaya geçilmeden önce çok merkezli dış doğrulama çalışmaları ile alternatif algoritmalar ve mevcut skorlama sistemleriyle karşılaştırmalı analizlerin yapılması gerekmektedir. Anahtar Kelimeler Akut apandisit; veri madenciliği; karar ağacı; makine öğrenmesi; tanısal öngörü; klinik karar desteği.

Anahtar Kelimeler

• Akut apandisit
• veri madenciliği
• karar ağacı
• makine öğrenmesi
• tanısal öngörü
• klinik karar desteği

Proje Numarası

12022015

Kaynakça

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