Medikal Sentetik Veri Üretimiyle Veri Dengelemesi

Abstract

Sağlık hizmetleri planlaması, klinik deneyler ve araştırma geliştirme çalışmaları gibi sağlık verisi kullanımını gerektiren alanlarda, kişisel sağlık verisinin elde edilmesi ve kullanımında etik, bürokratik ve operasyonel zorluklar yaşanmaktadır. Elektronik kişisel sağlık kayıtlarının güvenliği ve kişisel veri mahremiyeti konularındaki kısıtlamalar başta olmak üzere, klinik ve saha çalışmalarından veri elde edilmesinin maliyetli ve zaman alıcı olması, gerçek veriye en yakın şekilde yapay veri üretilmesini gerekli kılmaktadır. Bu çalışmada, son dönemde sağlık alanında artan veri kullanımı ihtiyacı doğrultusunda, sentetik veri kullanımının önemi ele alınarak, sentetik veri üretiminde kullanılan SMOTE, SMOTEENN, BorderlineSMOTE, SMOTETomek ve ADASYN yöntemlerinin performanslarının karşılaştırılması amaçlanmıştır. Çalışmada, gözlem ve sınıf sayısı birbirinden farklı ve ikisi de kamuya açık, 390 hastaya ait 15 değişkenden oluşan veri seti ile 19.212 COVID-19 hastasına ilişkin 16 değişkenden oluşan veri seti kullanılmıştır. Çalışma sonucunda SMOTE tekniğinin gözlem ve sınıf sayısının fazla olduğu veri setini dengelemede daha başarılı olduğu ve sentetik veri üretiminde hibrit tekniklere göre etkin olarak kullanılabileceği sonucuna ulaşılmıştır.

Keywords

• sentetik veri
• smote
• smoteenn
• sağlık bilişimi

Data Balancing with Synthetic Medical Data Generation

Abstract

There are ethical, bureaucratic and operational difficulties in obtaining and using personal health data in the areas that require the use of sensitive health data such as health care planning, clinical trials and research and development studies. The cost and time consuming of obtaining data from clinical and field studies, especially the restrictions on the security of electronic personal health records and personal data privacy, necessitate the production of synthetic data as close to real data. In this study, it is aimed to compare the performances of SMOTE, SMOTEENN, BorderlineSMOTE, SMOTETomek and ADASYN methods that have been used in synthetic data production by considering the importance of synthetic data generation in line with the increasing need for data use in the health field. In the study, a dataset consisting of 15 variables belonging to 390 patients with different observation and class numbers and a dataset consisting of 16 variables related to 19,212 COVID-19 patients were used. It has been concluded that SMOTE is more successful in balancing the data sets with large number of observations and multiclass classification. This technique can be used effectively in synthetic data generation compared to hybrid techniques.

Keywords

• synthetic data
• smote
• smoteenn
• health informatics

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