Enhancing Governmental Decision-Making through Predictive Analytics with Machine Learning-Based Data-Driven Framework

Aug 2, 2026

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Government bodies worldwide are digitizing and adopting data-driven technologies to achieve decisions that are faster, more transparent, and better informed. Machine learning (ML) is widely used due to its predictive capability, enabling governments to uncover previously hidden trends and forecast plausible future scenarios with measurable certainty. Predictive analytics supports high-impact domains including public finance, healthcare planning, emergency management, and resource allocation, while the paper evaluates multiple ML models on real public datasets using RMSE, MAE, and R² metrics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Predictive analytics in public sector

Challenges in integrating ML

Literature Review

Babylonian Journal of Machine Learning Vol.2025, pp. 86–96 DOI: [https://doi.org/10.58496/BJML/2025/007](https://doi.org/10.58496/BJML/2025/007) ; ISSN: 3006–5429 [https://mesopotamian.press/journals/index.php/BJML](https://mesopotamian.press/journals/index.php/BJML) Research Article Enhancing Governmental Decision-Making through Predictive Analytics with Machine Learning-Based Data-Driven Framework Mahdi Salah Mahdi AL-Inizi 1,* 1 Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey. ARTICLEINFO Article History Received 27 Mar 2025 Revised 28 Apr 2025 Accepted 25 May 2025 Published 29 Jun 2025 Keywords Artificial Intelligence (AI) Machine Learning (ML) Predictive Algorithms Government Decision Making (GDM) Public Sector Innovation (PSI) Smart Governance (SG) Digital Transformation (DT) ABSTRACT Government bodies around the world are going digital and slowly starting to make use of data driven technologies to make better, faster and more transparent decisions. From these technologies, machine learning (ML) has become one of the most significantly employed tools, especially via its ability to predict. Predictive analytics allows governments to identify obscure trends that previously were hidden, predict potential future scenarios with an acceptable level of certainty and better inform decision-making in important areas, such as public finance, healthcare planning, emergency management, and resource allocation. In this work we explore the use of predictive modeling (implemented as our own Linear Regression, Decision Trees, Random Forests and Artificial Neural Networks) in the context of governmental decision models. The models were tested on real-world cases such as quarterly budget planning or estimation of healthcare service demand or emergency resource allocation using publicly available data from open government data platforms. Performance was evaluated based on the wellknown RMSE, MAE and R² score. Results show that Artificial Neural Network always leads the highest in predictive accuracy, especially in dense or complex data setting, and there is no significant difference between Random Forest and Neural Network (the Random Forest has more generalization between interpretability and predictive power. On the other hand, Linear Regression and Decision Trees are more interpretable but have restrictions in using non-linear or high-dimensional datasets. In addition, the paper covers practical challenges including algorithmic bias, data quality considerations, and infrastructure capabilities, and ethical implications of automated decision making. This study has implications for the growing smart governance by proposing an integrated machine learning framework suitable for evidence-based policymaking. Future work involves improving the accuracy of prediction by incorporating explainable AI methodologies and customizing the model locally to enhance transparency, accountability, and generalization across different regional offices. 1. INTRODUCTION Amid a digital revolution, government agencies are looking more and more at cutting-edge technology to power data-driven decision-making. Of these, Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become a set of innovative techniques to empower the creation of predictive systems to enhance the quality of public service delivery, allocation of resources and strategic planning. ML methods have the potential to allow governments move from reactionary governance to proactive (predictive governance), predicting future events/modification of policy on the fly based on realtime patterns in data [1] . Predictive analytics, a subset of ML, enables public sector organizations to examine past data and find actionable insights to make informed decisions. Approaches such as Linear Regression (LR), Decision Trees (DT), Random Forests (RF), Artificial Neural Networks (ANNs) are more and more used to model comple

FAQs

How does predictive analytics support governmental decision-making?

It uses past data to derive actionable insights that inform decisions. The approach helps identify trends and forecast future scenarios for areas like finance, healthcare, emergencies, and resource allocation.

Which machine learning models are evaluated in the study?

The study tests predictive modeling using Linear Regression, Decision Trees, Random Forests, and Artificial Neural Networks. Performance is assessed with RMSE, MAE, and R².

What challenges are discussed when implementing ML in government settings?

Key challenges include data fragmentation, infrastructure constraints, algorithmic bias, model interpretability, and data privacy. The paper also highlights ethical AI deployment and the need for human involvement to maintain public trust.

Aug 2, 2026

Upload by Quinn