Optimizing K-means Clustering with Seed Initialization for Osteoporosis Diagnosis Based on Family History - Mahiruna, Adiyah; Ngatimin, Ngatimin; Destriana, Rachmat (International Journal of Management Science and Information Technology, 2026)
World Osteoporosis Day (WOD) is celebrated on October 20 every year, to raise global awareness about the prevention, diagnosis, and treatment of osteoporosis. Urgency in Indonesia, the number of elderly people is projected to reach 71 million people in 2050, which will have an impact on increasing cases of osteoporosis. Therefore, the recommendations based on scientific evidence in this study aim to assist practitioners in preventing osteoporosis in adults and children. This study proposes a method of Improving K-Means Performance through Seeds. The performance of the K-Means clustering algorithm is highly dependent on the random selection of initial centroids, which can lead to unstable clusters, suboptimal local solutions, and increased iterations, particularly in medical datasets such as osteoporosis diagnosis based on family history. Therefore, there is a need for an optimized centroid initialization strategy that can improve clustering accuracy and stability without increasing computational complexity. The dataset used is the osteoporosis dataset as a testing dataset that can be accessed publicly Osteoporosis dataset. The novelty of this study lies in the introduction of Modified Average (MA) approach for centroid initialization, which eliminates random seed dependency and improves clustering stability without increasing computational complexity. From the results of nine experiments with the benchmarking dataset, it can be seen that the method proposed in this study indicates that practically the Proposed method has a tendency to perform better in Rand Index measurement compare to k-means in random seeds.
Keywords: K-means; Seeds; Clustering; Osteoporosis; Rand index