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

Handan Tanyıldızı Kökkülünk

Makale detayları

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

Yazarlar:

Handan Tanyıldızı Kökkülünk

Yayıncı:

Istanbul University

Ana Başlıklar:

Clinical Sciences (Other)

Dergi:

Journal of Advanced Research in Health Sciences Vol.6 Issue 3, pp.229-233

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311833

pISSN:

2651-4060

Anahtar kelimeler:

Özet:

 Objective: In this study, the aim was to make a categorical estimation of the absentpresence 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 healthyliver diseased individuals. Conclusion: Multiple linear regression is a preferable method for categorical disease classification.

 Objective: In this study, the aim was to make a categorical estimation of the absentpresence 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 healthyliver diseased individuals. Conclusion: Multiple linear regression is a preferable method for categorical disease classification.

 Objective: In this study, the aim was to make a categorical estimation of the absentpresence 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 healthyliver diseased individuals. Conclusion: Multiple linear regression is a preferable method for categorical disease classification.

 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 sağlıklıkaraciğer hastası bireylerde güçlü bir ayırıcı parametre olduğu tespit edilmiştir. Sonuç: Çoklu doğrusal regresyonun, kategorik hastalık sınıflandırması için tercih edilebilir bir yöntem olduğu bulunmuştur.

Makale detayları

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

Yazarlar:

Handan Tanyıldızı Kökkülünk

Yayıncı:

Istanbul University

Ana Başlıklar:

Clinical Sciences (Other)

Dergi:

Journal of Advanced Research in Health Sciences Vol.6 Issue 3, pp.229-233

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311833

pISSN:

2651-4060

Machine learning

liver

classificationMakine öğrenmesi

karaciğer

sınıflandırma

Sayı

Makale detayları

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

Yazarlar: Handan Tanyıldızı Kökkülünk

Yayıncı: Istanbul University

Ana Başlıklar: Clinical Sciences (Other)

Dergi: Journal of Advanced Research in Health Sciences Vol.6 Issue 3, pp.229-233

Belge Tipi: Makale

Yayın Tarihi: 10/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001311833

pISSN: 2651-4060

Machine learning

liver

classificationMakine öğrenmesi

karaciğer

sınıflandırma

Sayı

Yazarlar:

Handan Tanyıldızı Kökkülünk

Yayıncı:

Istanbul University

Ana Başlıklar:

Clinical Sciences (Other)

Dergi:

Journal of Advanced Research in Health Sciences Vol.6 Issue 3, pp.229-233

Belge Tipi:

Makale

Yayın Tarihi:

10/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311833

pISSN:

2651-4060