FAIR Machine Learning Model Pipeline Implementation of COVID-19 Data

Data Intelligence, vol. 4, pp. 971–990

Abstract

Abstract Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines (that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and (re)usability of data, so that new and robust analytical tools, such as machine learning (ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.

Authors 7

1. Sakinat Oluwabukonla Folorunso corresponding

   Olabisi Onabanjo University

   Affiliation as printed

   Department of Mathematical Sciences, Olabisi Onabanjo University, P.M.B 2002, Ago-Iwoye, Ogun State, Nigeria 120005, Nigeria

2. Ezekiel Adebayo Ogundepo
   Affiliation as printed

   Data Science Nigeria, Lagos 105102, Nigeria

3. Mariam Basajja Aachen

   Leiden University

   Affiliation as printed

   Leiden University, 1011NC, Amsterdam, the Netherlands

4. Joseph Bamidele Awotunde

   University of Ilorin

   Affiliation as printed

   Department of Computer Science, University of Ilorin, Ilorin, Kwara State, 240103, Nigeria

5. Abdullahi Abubakar Kawu

   Ibrahim Badamasi Babangida University

   Affiliation as printed

   Department of Computer Science, Ibrahim Badamosi University, Lapai, Niger State, 911101, Nigeria

6. Francisca Onaolapo Oladipo

   Kampala International University · Federal University Lokoja

   Affiliation as printed

   Federal University Lokoja, Nigeria

   Kampala International University, 260101, Uganda

   Virus Outbreak Data Network-Africa

7. Ibrahim Abdullahi

   Ibrahim Badamasi Babangida University

   Affiliation as printed

   Department of Computer Science, Ibrahim Badamosi University, Lapai, Niger State, 911101, Nigeria