Analisis Regresi Logistik Biner pada Penyakit Polycystic Ovary Syndrome (PCOS)

Abstract

Binary logistic regression is a statistical analysis technique that is useful for analyzing the relationship between independent variables and dependent variables that are binary. In the health field, binary logistic regression can be used to analyze the influence of lifestyle, disease diagnosis, health risk evaluation, and understanding disease distribution, including Polycystic Ovary Syndrome (PCOS). Polycystic Ovary Syndrome is a complex endocrine disorder with many risk factors and complications that commonly occur in women of reproductive age. The purpose of this study was to determine the factors that significantly influence Polycystic Ovary Syndrome using binary logistic regression. This study used Polycystic Ovary Syndrome data obtained from the kaggle website, with a sample of 300 patients and consisting of 8 independent variables. The result showed that three independent variables significantly influenced Polycystic Ovary Syndrome, namely body mass index for the obesity category (X^2_3), diabetes (X_3) and Anti Müllerian Hormone levels (X_7).