Causality with machine learning using the Lububu method for the diagnosis of African swine fever (ASF)

Steven Lububu
Additional contact information
Steven Lububu: Cape Peninsula University of Technology

International Journal of Business Ecosystem & Strategy (2687-2293), 2025, vol. 7, issue 2, 184-206

Abstract: This paper presents the practical application of a novel approach, the so-called "Lububu method", to develop a causal machine learning model (CML) for the diagnosis of African swine fever (ASF). The Lububu method was developed to build causal machine learning models by focusing on the contextual understanding of a particular phenomenon and using technological tools to improve accuracy. Its main goal is to identify cause-and-effect relationships that can lead to better outcomes in various fields, including manufacturing, energy production, agriculture, transportation, data management, medicine and computer science. In this study, the Lububu method serves as an experimental framework for the construction of a CML model tailored to ASF diagnosis. This involves gathering comprehensive knowledge about ASF, covering aspects such as the causes, symptoms, transmission patterns and diagnostic procedures. This detailed contextual understanding supports the development of a model that can accurately identify ASF-related factors, ultimately increasing diagnostic effectiveness. By combining AI innovation and epidemiological expertise, this approach redefines ASF diagnostics and paves the way for data-driven, ethical and globally applicable solutions for veterinary medicine. Key Words:Lububu method, data selection, quantitative research methods

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.bussecon.com/ojs/index.php/ijbes/article/view/745/435 (application/pdf)
https://doi.org/10.36096/ijbes.v7i2.745 (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:adi:ijbess:v:7:y:2025:i:2:p:184-206

Access Statistics for this article

International Journal of Business Ecosystem & Strategy (2687-2293) is currently edited by Umit Hacioglu

More articles in International Journal of Business Ecosystem & Strategy (2687-2293) from Bussecon International Academy Bussecon International Academy, School of Business, IHU, Ordu cad. F-05 Blok No 3, 34480 Basaksehir, Istanbul, Turkey. Contact information at EDIRC.
Bibliographic data for series maintained by Umit Hacioglu ().