Classification of Chronic Disease Status of Individuals Using Machine Learning Methods: An Application on The 2023 Income and Living Conditions Survey of the Turkish Statistical Institute

Öz

The increasing prevalence of chronic diseases and their negative impact on the quality of life of individuals is one of the priority issues in the field of public health. Early diagnosis and management of these diseases is a process complicated by inequalities in access to healthcare services and socio-economic factors. In this context, machine learning methods offer significant potential for making predictions by extracting information from large and complex data sets. In particular, the TabNet method stands out for its strong predictive capabilities and ability to model complex relationships. This study aims to classify the chronic disease status of individuals using methods such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Random Forest, Gradient Boosting and TabNet using data from the 2023 Income and Living Conditions Survey of the Turkish Statistical Institute. The results showed that overall access to health services is good, but some segments still have difficulty accessing it; chronic diseases have a strong relationship with factors such as general health status and employment; and the TabNet method can perform effective classification with performance metrics such as high accuracy, precision and sensitivity. As a result, the model successfully classified chronic disease status with an overall accuracy rate of 97%. This study provides valuable information that can be used to make strategic decisions for health policy development and sectoral analysis, and highlights the importance of using machine learning methods, particularly the TabNet technique, effectively in health data analysis.

Anahtar Kelimeler

• Machine Learning
• Data Classification
• Chronic Disease Management
• Health Policy Development
• TabNet

Makine Öğrenimi Yöntemleri ile Bireylerin Kronik Hastalık Durumlarının Sınıflandırılması: Türkiye İstatistik Kurumu’nun 2023 Gelir ve Yaşam Koşulları Araştırması Üzerine Bir Uygulama

Öz

Kronik hastalıkların artan prevalansı (görülme sıklığı) ve bunların bireylerin yaşam kalitesi üzerindeki olumsuz etkileri, kamu sağlığı alanında öncelikli meseleler arasında yer almaktadır. Bu hastalıkların erken teşhis ve yönetimi, sağlık hizmetlerine erişimdeki eşitsizlikler ve sosyoekonomik faktörlerle karmaşıklaşan bir süreçtir. Bu bağlamda, makine öğrenimi yöntemleri, büyük ve karmaşık veri kümelerinden bilgi çıkararak tahminlerde bulunma konusunda önemli bir potansiyel sunmaktadır. Özellikle TabNet yöntemi, güçlü tahmin yetenekleri ve karmaşık ilişkileri modelleme kapasitesi ile dikkat çekmektedir. Bu çalışma, Türkiye İstatistik Kurumu’nun 2023 Gelir ve Yaşam Koşulları Araştırması verilerini kullanarak, Yapay Sinir Ağları (YSA), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Destek Vektör Makinesi (DVM), Rastgele Orman, Gradient Boosting ve TabNet gibi yöntemler ile bireylerin kronik hastalık durumlarının sınıflandırılmasını amaçlamaktadır. Bulgular, sağlık hizmetlerine genel erişimin iyi olduğunu, ancak bazı kesimlerin hala erişimde zorluklar yaşadığını; kronik hastalıkların genel sağlık durumu ve istihdam gibi faktörlerle güçlü bir ilişkisi olduğunu ve TabNet yönteminin yüksek doğruluk, kesinlik ve duyarlılık gibi performans metrikleri ile etkili bir sınıflandırma yapabildiğini ortaya koymuştur. Sonuç olarak model, %97 genel doğruluk oranı ile kronik hastalık durumunu başarıyla sınıflandırmıştır. Bu çalışma, sağlık politikalarının geliştirilmesi ve sektörel analizler için stratejik kararlar alınmasında kullanılabilecek değerli bilgiler sunmakta ve makine öğrenimi yöntemlerinin, özellikle TabNet tekniğinin, sağlık verileri analizinde etkin bir şekilde kullanılmasının önemini vurgulamaktadır.

Anahtar Kelimeler

• Makine Öğrenimi
• Veri Sınıflandırma
• Kronik Hastalık Yönetimi
• TabNet
• Sağlık Politikaları Geliştirme

Kaynakça

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