Analisis Algoritma Klasifikasi Untuk Mengidentifikasi Potensi Risiko Kesehatan Ibu Hamil

Abstract

The health of pregnant women has an important aspect in efforts to achieve the birth of a healthy baby. So early detection of the health of pregnant women has important. In this study the author identified potential maternal health risks for pregnant women by classifying them used machine learning which aims to analyze maternal health datasets with several algorithms including Random Forest, Extra Trees, Extreme Gradient Boosting, Decision Tree, and Light Gradient Boosting Machine. From several classification results carried out analysis and evaluation shown that the Random Forest classification algorithm provided optimal performance with an accuracy of 82,15%. These findings confirmed that the model created could identify complex patterns and relationships between features relevant to the classification of potential health risks for pregnant women at high, medium and low levels. These results have important implications in maternal care, because they cann help doctors and medical personnel make more appropriate and effective decisions in dealing with maternal health risks and provide insight into pregnant women from an early age regarding their health conditions.

Keywords

• kesehatan ibu hamil, klasifikasi, hyperparameter tuning.