Pankreas Kanseri Teşhisinde İdrar Biyobelirteçlerinin Düzenlileştirme Yaklaşımlarına Dayalı Lojistik Regresyonla Sınıflandırma Kabiliyeti: Metodolojik Çalışma

Abstract

Objective: This study aims to compare the accuracy, reliability, and validity levels of the techniques by using various performance measures applying logistic regression models based on regularization approaches from data mining classification techniques on a dataset. Material and Methods: With the development of computerization and technology, machine learning is used in many fields as well as in the field of medicine. It has grown in popularity, particularly in cancer diagnosis. A urine biomarkers dataset from the public platform Kaggle database, which is freely available to all researchers, was used to reveal the most appropriate model for diagnosing patients' pancreatic ductal adenocarcinoma (PDAC). Because of the multicollinearity, the following regression models were considered to classify the disease diagnosis: Logistic lasso, logistic ridge, logistic elastic net, logistic adaptive lasso, logistic adaptive elastic net, and logistic adaptive group lasso. The classification success of the methods used was compared using reliability and validity criteria. Results: There were three statistically significant variables in all logistic regularization models, according to PDAC diagnostic results. Compared to the estimated model results, the logistic adaptive group lasso regression model appears to perform better in PDAC diagnosis. In addition to the three variables in this model, the variables age and plasma CA19-19 have been identified as important variables in PDAC diagnosis. Conclusion: As a result of comparative analyses, the logistic adaptive group lasso regression model outperformed the others in terms of performance measures.