The need of similarity measures in life science is ever paramount given the modern biotechnology in producing and storing biomedical datasets in large amounts. This paper presents a novel scheme in measuring similarity of two datasets by prediction class, namely SPC. SPC offers an alternative approach to traditionally used ones such as pairwise correlations which assume every attribute carries equal importance. The unique advantage of SPC is the use of a machine learning model called Fuzzy Unordered Rule Induction to infer the similarity between two datasets based on their common attributes and their degrees of relevance pertaining to a predicted class. The method is demonstrated by a case of comparing lung cancer dataset and heart disease dataset.