Research Analysis for Detecting Duplicate Entries in Electoral Voter List

Abstract

Electoral voter lists form the backbone of democratic processes, ensuring that every eligible citizen is granted a fair opportunity to vote. However, large-scale voter databases often suffer from data quality issues such as duplicate or near-duplicate entries caused by spelling variations, data entry errors, migration, and inconsistent demographic updates. These redundancies can compromise the integrity of elections, increase administrative costs, and reduce public trust. This research proposes an intelligent, analytics-driven framework for detecting duplicate entries in electoral voter lists using a combination of data pre processing techniques, rule-based matching, and machine learning models. The proposed system integrates phonetic similarity, demographic attribute comparison, and supervised classification models to identify potential duplicates with high accuracy. Experimental analysis demonstrates that the hybrid approach significantly outperforms traditional exact-matching techniques, offering a scalable and reliable solution for election management bodies. The study emphasizes transparency, accuracy, and scalability while maintaining compliance with ethical and data privacy considerations.

Introduction

The text presents a machine learning–based analytical framework for detecting duplicate entries in electoral voter lists, addressing a critical challenge in maintaining accurate and fair democratic databases. Large-scale voter registries are prone to duplication due to spelling variations, inconsistent address formats, migration, delayed updates, and manual data entry errors—issues that traditional exact-matching and manual verification methods cannot handle effectively.

The proposed approach replaces purely deterministic or manual methods with an intelligent, automated pipeline that combines data preprocessing, similarity-based feature engineering, blocking strategies, and supervised machine learning classification. Preprocessing standardizes noisy and inconsistent data, while feature engineering captures phonetic, string, demographic, and geographic similarities to identify near-duplicate records. Blocking techniques reduce computational complexity, making the system scalable for millions of records.

Supervised ensemble learning models generate probability scores for potential duplicates, enabling flexible, threshold-based decisions. High-confidence cases can be automatically flagged, while ambiguous records are reviewed through a human-in-the-loop process to ensure transparency, accountability, and ethical compliance.

Overall, the framework improves accuracy, scalability, and efficiency in voter list management, supports proactive data quality control, and balances automation with human oversight to maintain trust, fairness, and integrity in electoral systems.

Conclusion

This research presents an intelligent and scalable framework for detecting duplicate entries in electoral voter lists using data analytics and machine learning techniques. The proposed system effectively addresses the limitations of traditional rule-based approaches by handling data inconsistencies, spelling variations, and large-scale datasets. The integration of similarity-based features and supervised learning models enhances detection accuracy while maintaining operational efficiency. The findings suggest that the proposed framework can significantly support election management bodies in maintaining clean and reliable voter databases. By reducing redundancy and improving data quality, the system contributes to administrative efficiency and public trust in electoral processes. Future research may focus on incorporating deep learning-based similarity representations, multilingual name handling, and real-time voter list updates. Additionally, extending the framework to include explainable AI techniques can further enhance transparency and acceptance among stakeholders. Integration with national identity systems, while maintaining strict privacy safeguards, also presents a promising direction for future work. Future enhancements may include the incorporation of multilingual text processing to handle regional language variations, adaptive learning mechanisms that update models based on new data patterns, and integration with real-time voter registration platforms. Expanding explainability features will further support trust and transparency in automated electoral data management systems. Future work may explore the integration of deep learning-based similarity models, multilingual processing capabilities, and real-time duplicate detection during voter registration. Additionally, incorporating explainable artificial intelligence techniques can further enhance transparency and acceptance among stakeholders. The proposed framework provides a strong foundation for developing next-generation electoral data management systems that are accurate, ethical, and scalable.

References

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Copyright

Copyright © 2026 Krish Pritesh Keer, Prof. Suhas Rautmare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.