Prediction of high-risk human papillomavirus after conization by machine learning methods

Abstract

Objectives This study aimed to use machine learning, a branch of artificial intelligence, to predict the persistence of high-risk HPV in women who have undergone conization surgery. Materials and Methods: This retrospective study was conducted between 2018 and 2023 in the Gynecology and Obstetrics Clinic of Balıkesir University Health Practice and Research Hospital. A dataset of 69 female patients between the ages of 23-67 years; for the prediction of HPV status 1 year after the conization operation, the patients' data were recorded according to the criteria we determined, and these data were analyzed and classified using machine learning methods. Various Machine Learning methods such as Gradient Boosting, Support Vector Machine (SVM), Catboost, Random Forest (RF), and Naive Bayes (NB) are used here. Results: We found the highest accuracy rate in Random Forest, and Catboost with 76 %. Gradient Boosting followed with a score of 67%, and Naive Bayes and Support Vector Machine (SVM) performed considerably lower, with scores of 48% and 43%, respectively. Conclusions: Our results show that machine learning, a novel use of artificial intelligence, is effective in predicting the persistence of high-risk HPV. Further studies with more data will be a promising and useful tool for HPV and cervical cancer screening in the future.

Keywords

• Cervical intraepithelial neoplasia
• human papillomavirus
• artificial intelligence
• machine learning method

Konizasyon sonrası yüksek riskli insan papilloma virüsünün makine öğrenmesi yöntemleriyle tahmini

Abstract

Amaç: Bu çalışma, konizasyon ameliyatı geçiren kadınlarda yüksek riskli HPV'nin kalıcılığını tahmin etmek için yapay zekanın bir dalı olan makine öğrenimini kullanmayı amaçladı. Gereç ve Yöntem: Bu retrospektif çalışma 2018-2023 yılları arasında Balıkesir Üniversitesi Sağlık Uygulama ve Araştırma Hastanesi Kadın Hastalıkları ve Doğum Kliniğinde gerçekleştirildi. 23-67 yaş arası 69 kadın hastadan oluşan veri seti; Konizasyon operasyonundan 1 yıl sonra HPV durumunun tahmini için hastaların verileri belirlediğimiz kriterlere göre kayıt altına alındı ​​ve bu veriler makine öğrenmesi yöntemleri kullanılarak analiz edildi ve sınıflandırıldı. Burada Gradient Boosting, Support Vector Machine (SVM), Catboost, Random Forest (RF) ve Naive Bayes (NB) gibi çeşitli Makine Öğrenimi yöntemleri kullanılmaktadır. Bulgular: En yüksek doğruluk oranını %76 ile Random Forest ve Catboost'ta bulduk. Bunu %67 puanla Gradient Boosting takip ederken, Naive Bayes ve Support Vector Machine (SVM) sırasıyla %48 ve %43 puanlarla oldukça düşük performans gösterdi. Sonuçlar: Sonuçlarımız, yapay zekanın yeni bir kullanımı olan makine öğreniminin, yüksek riskli HPV'nin kalıcılığını tahmin etmede etkili olduğunu göstermektedir. Daha fazla veri içeren ileri çalışmalar gelecekte HPV ve rahim ağzı kanseri taraması için umut verici ve faydalı bir araç olacaktır.

Keywords

• Servikal intraepitelyal neoplazi
• insan papilloma virüsü
• yapay zeka
• makine öğrenme yöntemi

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