Unsupervized Techniques to Identify Patterns in Gynecologic Information

Medical records are a source of valuable information. Processing enormous amounts of data effectively while looking for patterns of interest is made feasible by utilizing artificial intelligence and machine learning techniques. In this research, we apply clustering methods to identify patterns in the health records of women, including age, medical condition, illness, contraceptive methods, and gynecologic features. The methodology used includes data understanding, preprocessing, modeling, and evaluation. For data clustering, three unsupervised algorithms -k-means, DBSCAN, and hierarchical clustering-were applied. To evaluate each technique’s effectiveness, the silhouette metric was used. The experiment results highlight that the optimal silhouette value of 0.73 was achieved with the DBSCAN algorithm by grouping data into 9 clusters. These findings greatly advance our understanding of the most common genital infections and improve our capacity to identify unique patterns within each cluster.