Pediatrik apandisit tanı ve tedavisinde gelişmiş sonuçlar için makine öğreniminden yararlanma

Abstract

Pediatrik apandisit, kritik bir durum olarak, sunumundaki değişkenlik ve hem tanı hem de sonuç tahmini için spesifik bir biyobelirtecin olmaması nedeniyle hem tanı hem de tedavi yönetiminde klinik zorluklar sunar. Makine öğrenimi (ML) algoritmalarından yararlanan bu çalışma, Almanya, Regensburg'daki Çocuk Hastanesi St. Hedwig'den kapsamlı klinik veriler ve geniş bir hasta demografisi yelpazesi içeren sağlam bir veri setini kullanarak tanı doğruluğunu ve tedavi stratejilerini iyileştirmeyi amaçlamaktadır; pediatrik apandisitin tanısını, yönetimini ve ciddiyetini değerlendirmek için 10 katlı çapraz doğrulama kullanarak Çok Katmanlı Sinir Ağları (MLNN), Destek Vektör Makineleri (SVM) ve Doğrusal Ayırıcı Analiz (LDA) dahil olmak üzere üç ML tekniğinin verimliliğini değerlendirdik. Bulgular, SVM'nin mükemmel sınıflandırma puanları elde etme ölçütlerindeki üstünlüğünü, ardından MLNN'nin güçlü performansını ortaya koymaktadır. Tersine, doğrusal yapısı nedeniyle LDA, karmaşık veri setinde bulunan karmaşık ve doğrusal olmayan ilişkileri ele almak için yetersiz olduğu kanıtlanmıştır. Çalışma, pediatrik apandisit tedavisinin yönetimine bütünsel bir yaklaşım sağlayan ML destekli klinik karar destek sistemlerinin kullanılma potansiyelini vurgulamaktadır

Keywords

• Pediatrik Apandisit
• Klinik Karar Desteği
• Makine Öğrenimi

Leveraging machine learning for improved outcomes in pediatric appendicitis diagnosis and management

Abstract

Pediatric appendicitis, as a critical condition, represents clinical challenges in both diagnostic and treatment management due to the variability in its presentation and the absence of a specific biomarker for both diagnosis and outcome prediction. Leveraging Machine Learning (ML) algorithms, this study aims to improve diagnostic accuracy and treatment strategies utilizing a robust dataset from the Children’s Hospital St. Hedwig in Regensburg, Germany, containing extensive clinical data and a broad spectrum of patient demographics. We evaluated the efficiency of three ML techniques, including Multilayer Neural Networks (MLNN), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), using 10-fold cross-validation to assess the diagnosis, management, and severity of pediatric appendicitis. The findings reveal SVM’s consistently strong performance across all metrics, achieving highly accurate classification results, followed by the competitive performance of MLNN. Conversely, LDA demonstrated limitations due to its linear nature, proving insufficient for handling the intricate and nonlinear relationships present in the complex dataset. The study highlights the potential of using ML-powered clinical decision support systems, providing a holistic approach to the treatment management of pediatric appendicitis.

Keywords

• Pediatric Appendicitis
• Clinical Decision Support
• Machine Learning

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