A Methodology to Extract Knowledge from Datasets Using ML

This study aims to verify whether there is any relationship between the different classification outputs produced by distinct ML algorithms and the relevance of the data they classify, to address the problem of knowledge extraction (KE) from datasets. If such a relationship exists, the main objective of this research is to use it in order to improve performance in the important task of KE from datasets. A new dataset generation and a new ML classification measurement methodology were developed to determine whether the feature subsets (FSs) best classified by a specific ML algorithm corresponded to the most KE-relevant combinations of features. Medical expertise was extracted to determine the knowledge relevance using two LLMs, namely, chat GPT-4o and Google Gemini 2.5. Some specific ML algorithms fit much better than others for a working dataset extracted from a given probability distribution. They best classify FSs that contain combinations of features that are particularly knowledge-relevant. This implies that, by using a specific ML algorithm, we can indeed extract useful scientific knowledge. The best-fitting ML algorithm is not known a priori. However, we can bootstrap its identity using a small amount of medical expertise, and we have a powerful tool for extracting (medical) knowledge from datasets using ML.