SAĞLIKLI VE KARACİĞER HASTALIĞI OLAN BİREYLERİN DOĞRUSAL REGRESYON SINIFLANDIRMA ALGORİTMASIYLA TAHMİN EDİLMESİ

Yıl 2023, , 229 - 233, 24.10.2023

Handan Tanyıldızı Kökkülünk

https://doi.org/10.26650/JARHS2023-1231512

Öz

Amaç: Bu çalışmada sağlıklı ve karaciğer hastalığı olan bireylere ait bazı kan biyokimya parametreleri (ALB, ALP, ALT, AST, BIL, CHE, CHOL, CREA, GGT ve PROT), cinsiyet ve yaş bilgileri kullanılarak karaciğer hastalığı yok/ var kategorik tahmini yapılması amaçlanmıştır.
Gereç ve Yöntem: R Studio programında makine öğrenmesine ait çoklu doğrusal regresyon ile tahmin elde edilmiştir. Akaike bilgi kriteri kullanılarak tahmin üzerine yüksek katkısı olan parametreler seçilerek makine öğrenmesinde iyileştirilmeye gidilmiştir.
Bulgular: Tahmine pozitif yönlü etkisi olan en güçlü 3 parametre sırasıyla AST, BIL ve GGT; negatif yönlü etkisi olan en güçlü 3 parametre sırasıyla CHOL, CHE ve ALB bulunmuştur. Kullanılan modelin doğruluğu %91, kesinlik %99, geri çağırma 0,91 ve F skoru %94 olarak bulunmuştur. Korelasyon ilişkisi grafiği incelendiğinde AST ‘nin sağlıklı/karaciğer hastası bireylerde güçlü bir ayırıcı parametre olduğu tespit edilmiştir.
Sonuç: Çoklu doğrusal regresyonun, kategorik hastalık sınıflandırması için tercih edilebilir bir yöntem olduğu bulunmuştur.

Anahtar Kelimeler

Makine öğrenmesi, karaciğer, sınıflandırma

ESTIMATION OF HEALTHY AND LIVER DISEASED INDIVIDUALS BY A LINEAR REGRESSION CLASSIFICATION ALGORITHM

Öz

Objective: In this study, the aim was to make a categorical estimation of the absent/presence of liver disease by using some blood biochemistry parameters (ALB, ALP, ALT, AST, BIL, CHE, CHOL, CREA, GGT, and PROT), gender and the age of healthy individuals, and those with liver disease.
Material and methods: The prediction was obtained with multiple linear regression of machine learning in the R Studio program. Machine learning was improved by selecting parameters that have a high contribution to the prediction by using the Akaike information criterion.
Results: The three strongest parameters with a positive effect on the estimation were AST, BIL, and GGT, respectively; The three strongest parameters with negative effects were CHOL, CHE, and ALB, respectively. The accuracy of the model used was 91%, the precision was 99%, the recall was 0.91, and the F score was 94%. When the correlation relationship graph was examined, it was determined that AST was a strong differential parameter in healthy/liver diseased individuals.
Conclusion: Multiple linear regression is a preferable method for categorical disease classification.

Anahtar Kelimeler

Machine learning, liver, classification

Kaynakça

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