DATA MINING FOR MATERNAL CARE: A K-MEANS APPROACH TO DELIVERY DATA IN SLEMAN
Abstract
Childbirth is a critical physiological process experienced by women, necessitating proper care and attention for both maternal and fetal well-being. Adequate prenatal care and nutrition play a vital role in ensuring a healthy outcome for both mother and child. Regular check-ups during pregnancy, spanning from conception until the first 1000 days of a child's life, are essential to detect and address potential issues promptly, preventing stunting and promoting optimal health. However, the availability and accessibility of healthcare facilities, along with the assistance of trained medical professionals, significantly influence childbirth outcomes. Unfortunately, some regions, such as certain provinces in Indonesia, continue to experience low percentages of deliveries assisted by health personnel, falling below the national average.
To address these challenges and gain insights into decision-making and problem-solving, this study focuses on applying the K-means clustering algorithm to analyze delivery data from the Sleman Regency of Indonesia between 2018 and 2019. Clustering is a popular technique for managing extensive data due to its simplicity and effectiveness in various fields. Previous studies have successfully applied the K-means algorithm in diverse domains, such as breast cancer diagnosis, product grouping, and decision support systems for retail stores. These studies have demonstrated improved accuracy and efficiency in their respective analyses.
In this research, we aim to explore and compare the number of deliveries assisted by medical staff in the specified period using the K-means algorithm. By leveraging the power of data mining and clustering techniques, we seek to identify patterns and groupings within the delivery data, which can inform healthcare professionals and policymakers. The insights gained from this study can contribute to better resource allocation, enhanced healthcare services, and improved maternal and child health outcomes. Ultimately, the findings can aid in reducing maternal mortality rates by ensuring timely and appropriate medical interventions during childbirth.
