MSKP: A Proposed K-Prototypes Clustering Algorithm for Multi-Source Medical Data

Effective clustering of medical data is essential for the progress of precision medicine and data-driven healthcare solutions. Modern medical datasets are increasingly complex, often integrating multi-source data with both categorical and numerical features of varying importance. However, existing algorithms like the k-prototype could not handle ordered features and fail to account for the differing importance of multi-source data in the clustering process. This study introduces a proposed k-prototype algorithm that incorporates weights to cluster medical data from multi-sources. In particular, we initially present an enhanced cost function to consistently evaluate the distance between ordered, categorical, and numerical features. Then, weights are allocated to each feature using a multi-objective optimization algorithm, and the optimal weighting operator is identified through an optimization search within the evolutionary computation framework. Experiments on simulated and real medical datasets show the algorithm's superior performance. It offers a robust solution for multi-source medical data analysis. This improvement enhances insights and supports better decision-making in medical data analysis.