Riset Data Risiko Kehamilan Menggunakan Decision Tree Dari Dataset Risk Maternal Health

Abstract

Pregnancy is a crucial stage in a woman's life that requires special attention to ensure the safety and health of both mother and fetus. This study aims to analyze potential health risks in pregnant women by applying the Decision Tree method to the Maternal Health Risk dataset. Poor health in pregnant women can have fatal consequences, including maternal and child mortality. Therefore, a systematic approach is needed to predict and manage these risks. The Decision Tree method was chosen because of its ability to produce clear and easy-to-understand models for health data classification. In this study, maternal health data was analyzed using the Decision Tree algorithm, which achieved 100% accuracy. These findings indicate that the resulting decision tree model can be used as a tool in the decision-making process related to health risk classification in pregnant women. Thus, this study makes a significant contribution to efforts to prevent undesirable health conditions during pregnancy.