Understanding Parental Characteristics of Child Adoption Candidates using MMPI-2 and Evolutionary Clustering

In the context of adoption, evaluating prospective adoptive parents using psychometric assessments such as the Minnesota Multiphasic Personality Inventory (MMPI) questionnaire is essential for understanding their psychological profiles. However, interpreting such complex data can be both challenging and time-consuming. In this study, we propose a meta-analysis tool to assist psychologists in their initial interpretation and analysis of MMPI-2 results by providing a clear data-driven visualization of key psychometric scales. Our system employs unsupervised learning techniques to uncover meaningful patterns and relationships in the data with minimal prior input. Specifically, a genetic algorithm is used to optimize clustering quality by selecting the most relevant psychological scales, enhancing cluster separation, and improving data interpretability. We also explored and compared the effectiveness of several clustering algorithms, including K-Means, Gaussian Mixture Model, and Spectral Clustering, to maximize the capabilities of our tool.