Objective: The aim of this study is to build personbased prediction models for simulated and real datasets separately with the SHapley Additive exPlanations method, and to demonstrate whether the obtained person-based models are more valid and applicable than overall models. Material and Methods: Simulated datasets encompassed 13 independent and 1 dependent variable, across sample sizes of 250, 500, and 1,000, while the real dataset contained 826 patient records with 11 variables. ''bindata'', ''shaper'' and ''RWeka'' packages in the R (version 4.1.2) programming language were used. Extreme Gradient Boosting, Bagging, Random Forest, Support Vector Machine and Logistic Regression were used as classification methods. The assessment employed 10-fold crossvalidation, repetaed 1,000 times. Results: Accuracy values of the overall model in the datasets with 250, 500, and 1,000 samples were found to be 0.856, 0.886, and 0.891, respectively. In these samples, the person-based accuracy values were found to be 0.886, 0.964, and 0.962 for those with ''yes'' prediction results, and 0.930, 0.961, and 0.961 for those with ''no'' prediction results, respectively. In the real dataset, the accuracy of the overall model was found to be 0.736. The person-based accuracy values were found to be 0.783 in the patient who was predicted with stroke, and 0.868 in the patient who was predicted without stroke. Conclusion: Personbased predictions consistently outperformed model-based results across datasets due to real-life individual heterogeneity, emphasizing the need for attention. Considering this diversity, person-based modeling is expected to produce a more realistic and clinically applicable model.
Keywords: Prediction; person-based prediction models; SHapley Additive exPlanations
Amaç: Bu çalışmanın amacı, ''SHapley Additive exPlanations'' yöntemi ile simüle ve gerçek veri setleri için ayrı ayrı kişi temelli tahmin modelleri oluşturmak ve elde edilen kişi temelli modellerin genel modellere göre daha geçerli ve uygulanabilir olup olmadığını göstermektir. Gereç ve Yöntemler: Simüle veri setleri sırasıyla 250, 500 ve 1.000 örneklem büyüklükleriyle 13 bağımsız ve 1 bağımlı değişken içerirken, gerçek veri seti 11 değişkenden oluşmakta olup, 826 hasta verisi içermektedir. Analizler için R (versiyon 4.1.2) programlama dilindeki ''bindata'', ''shapper'' ve ''RWeka'' paketleri kullanılmıştır. Sınıflandırma yöntemleri olarak ''Extreme Gradient Boosting'', Bagging, Rastgele Orman, Destek Vektör Makinesi ve Lojistik Regresyon kullanılmıştır. Veri seti 10-kat çapraz doğrulama kullanılarak değerlendirilmiş ve analizler 1.000 kez tekrarlanmıştır. Bulgular: 250, 500 ve 1.000 örneklem büyüklüğüne sahip veri setlerinde genel modelin doğruluk değerleri sırasıyla 0,856, 0,886 ve 0,891 olarak bulunmuştur. Bu örneklem büyüklüklerinde kişi temelli doğruluk değerleri ''evet'' tahmin sonucuna sahip olanlar için sırasıyla 0,886, 0,964 ve 0,962; ''hayır'' tahmin sonucuna sahip olanlar için ise sırasıyla 0,930, 0,961 ve 0,961 olarak bulunmuştur. Gerçek veri setinde, genel modelin doğruluğu 0,736 olarak bulunmuştur. Kişi temelli doğruluk değerleri ise inme tahmini yapılan hastada 0,783, inme tahmini olmayan hastada ise 0,868 olarak bulunmuştur. Sonuç: Tüm veri setlerinde kişi temelli tahmin sonuçları, model bazlı sonuçlardan daha yüksek bulunmuştur. Bu gerçek hayatta kişiler arası heterojenite nedeniyle göz ardı edilmemesi gereken bir durumdur. Bu farklılık göz önünde bulundurularak, kişi temelli modelleme yapıldığında, modelin daha gerçekçi olacağı ve klinik kullanıma daha uygun hâle geleceği düşünülmektedir.
Anahtar Kelimeler: Tahmin; kişi temelli tahmin modelleri; SHapley Additive exPlanations
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