ESTIMATION OF HEALTHY AND LIVER DISEASED INDIVIDUALS BY A LINEAR REGRESSION CLASSIFICATION ALGORITHM - Handan Tanyıldızı Kökkülünk (Sabiad)
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.