Clustering Analysis of Reproductive Health Data

Reproductive health involves several phases such as fertility, menstrual cycles, and hormonal dynamics. Data-driven methodologies, like clustering analysis, help understand these complexities. This chapter discusses the different clustering algorithms that can be used on reproductive health data such as k-means, hierarchical, density-based clustering (e.g., DBSCAN), and model-based clustering (e.g., Gaussian mixture models). Furthermore, the chapter describes the preprocessing steps required to prepare reproductive data for clustering analysis focusing on the different techniques for feature selection, dimensionality reduction, and data transformation to enhance the quality and effectiveness of clustering results. It further discusses the importance of performance evaluation that could be computed using evaluation metrics which is an important determinant of the clustering algorithm’s efficiency. The text describes the techniques for evaluating clusters’ compactness, separation, and stability. In the last section of the chapter, various applications of clustering analysis are discussed with an interpretation of the application of clustering analysis in reproductive health.