PENGGUNAAN PROPENSITY SCORE STRATIFICATION-SUPPORT VECTOR MACHINE UNTUK MENGESTIMASI EFEK PERLAKUKAN AKTIVITAS OLAHRAGA PADA PENDERITA DIABETES MELITUS

Abstract

In a study it is necessary to have a good randomization role between the treatment and control groups so there is no large differences in the observed covariates resulting in an estimate of the effect of unbiased treatment. However, in observational studies, especially in the field of health, because it is directly related to human life, it is not possible to do Randomized Controlled Trial (RCT). One method of propensity score (PS) is Propensity Score Stratification (PSS) with approach of Support Vector Machine (SVM) is used to overcome the problem of bias due to non-random observation and unbalanced covariate. The case used in this research is disease complication in patient of Diabetes Mellitus Type 2 at Regional Public Hospital of Pasuruan with respondent counted 96 patient. The result is obtained of the analysis is the variables that become confounding is a sport activity. The accuracy level of PSS SVM is the same for all strata that is equal to 65.6%. Estimation of treatment effect (ATE) gave the result that the variable of sports activity is a variable that influence the disease complication (Y) in patients of DM type 2. The number of strata that reduce the largest bias is in strata of 4 with the percent bias reduction (PBR) is 86.39% with the smallest standard error value is 0.103 and the estimated value of ATE is 0.597.