A Methodology to Extract Knowledge from Datasets Using ML

Ricardo Sánchez- de-Madariaga (), Mario Pascual Carrasco and Adolfo Muñoz Carrero
Additional contact information
Ricardo Sánchez- de-Madariaga: Telemedicine and e-Health Research Unit, Instituto de Salud Carlos III, Monforte de Lemos 5, 28029 Madrid, Spain
Mario Pascual Carrasco: Telemedicine and e-Health Research Unit, Instituto de Salud Carlos III, Monforte de Lemos 5, 28029 Madrid, Spain
Adolfo Muñoz Carrero: Telemedicine and e-Health Research Unit, Instituto de Salud Carlos III, Monforte de Lemos 5, 28029 Madrid, Spain

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: 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.

Keywords: knowledge relevance; knowledge extraction; feature subset; large language models; machine learning algorithms; statistics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/11/1807/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/11/1807/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:11:p:1807-:d:1666844

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().