Work

GDP growth drivers in Saudi Arabia based on machine learning algorithms

Year: 2025

Type: article

Abstract: This study utilizes machine-learning algorithms to investigate the economic sectors that most significantly influence Saudi Arabia's economic growth rate, focusing on agriculture, industry, and services. The analysis shows that the random forest algorithm offers the highest predictive accuracy in identifying the key sectors driving economic growth. The research findings show that the service and industrial sectors account for 39.3% and 37.7% of Saudi Arabia's GDP growth, respectively. These results show that this country is moving significantly toward diversifying its economy as it depends more and more on non-oil sectors for growth. Even while the agricultural industry presently makes up a lower 23% of the total GDP, its comparison small share does not limit its potential for expansion. The paper emphasizes how agricultural developments, such as enhanced technologies and more efficient methods, could increase economic impact. The agricultural sector has the potential to play a significant role in boosting future economic growth, which would further help Saudi Arabia's objectives for sustainable growth and diversification.

Source: Journal of Radiation Research and Applied Sciences

DOI: 10.1016/j.jrras.2025.101380

Authors: Mohamed F. Abd El-Aal, Mansour Shrahili, Mohamed Kayid, Shahid Mohammad

Institutions: Arish University, King Saud University, University of Wisconsin–Oshkosh

Language: English

Topic: Economic Growth and Development

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Decent work and economic growth, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,400