Obesity Analysis and Prediction with Optimized Supervised Learning Algorithms

Yıl 2023, Cilt: 14 Sayı: 2, 301 - 312, 31.12.2023

Tülay Turan

Öz

Obesity is a health problem that is the fifth most important cause of death worldwide. In the report published in 2022, the World Health Organization (WHO) emphasized that obesity forms the basis of many diseases and can be stopped by following the necessary measures and policies. Therefore, obesity analysis and prediction applications with machine learning algorithms are important. In this study, prediction models were developed with K-Nearest Neighbor Algorithm (KNN) and Random Forest Algorithms (RF), which are supervised learning algorithms, using data from the UCI machine learning data repository. These models were compared using different statistical evaluation criteria. As a result of the evaluation, the RF model with hyperparameter optimization achieved the best prediction result with an average accuracy of 94%. The study is important because it analyzes and visualizes the factors affecting the prevalence of obesity and predicts its levels with a high success rate.

Anahtar Kelimeler

Obesity Analysis, Supervised Machine Learning, Hyperparameter Optimization, KNN, RF

Optimize Edilmiş Denetimli Öğrenme Algoritmaları ile Obezite Analizi ve Tahmini

Öz

Obezite dünya genelinde gerçekleşen ölümlerin en önemli beşinci nedeni olarak karşımız çıkan bir sağlık sorunudur. Dünya Sağlık Örgütü (DSÖ) 2022 yılında yayınladığı raporda obezitenin birçok hastalığın temelinin oluşturduğunu ve gerekli önlemler ve politikalar izlenerek durdurulabileceğini vurgulamıştır. Bu nedenle makine öğrenmesi algoritmaları ile obezite analizi ve tahmin uygulamaları önemlidir. Bu çalışmada, UCI makine öğrenmesi veri havuzundan alınan veriler kullanılarak, denetimli öğrenme algoritmalarından K-En Yakın Komşu Algoritması(KNN) ve Rastgele Ormanlar Algoritmaları(RF) ile tahmin modelleri geliştirilmiştir. Bu modeller farklı istatistiksel değerlendirme kriterleri kullanılarak karşılaştırılmıştır. Değerlendirme sonucunda hiper parametre optimizasyonu gerçekleştirilen RF modeli %94 ortalama accuracy(doğruluk) sonucu ile en iyi tahmin sonucunu elde etmiştir. Çalışma obezite prevalansını etkileyen faktörleri analiz etmesi, görselleştirmesi ve yüksek bir başarı oranı ile seviyelerini tahmin etmesiyle önemlidir.

Anahtar Kelimeler

Obezite Analizi, Denetimli Makine Öğrenmesi, Hiperparametre Optimizasyonu, KNN, RF

Kaynakça

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