Adaptive Data Mining Approaches for Efficient Predictive Analytics in Healthcare

Abstract

The healthcare sector is rapidly evolving with the integration of big data and machine learning. Predictive analytics enables early diagnosis, risk assessment, and efficient patient management. However, traditional data mining techniques are often static and cannot adapt to the continuous flow of medical data from wearable sensors, hospital systems, and remote diagnostics. This paper explores adaptive data mining approaches, emphasizing incremental learning, concept drift handling, and ensemble strategies. The study evaluates the performance of these approaches using real-world healthcare datasets, demonstrating their superiority in accuracy, speed, and adaptability compared to static models.

Introduction

The healthcare industry is experiencing a major shift due to the vast amounts of data generated from sources like electronic health records (EHRs), wearable devices, and telemedicine. Predictive analytics has become essential for improving patient outcomes and optimizing clinical decisions by forecasting disease progression, hospital readmissions, and personalizing treatments. However, traditional static data mining models struggle with the dynamic and continuously changing nature of healthcare data, which often exhibits concept drift — changes in data patterns over time due to factors like new diseases, seasonal variations, and evolving treatments.

To overcome these challenges, adaptive data mining techniques that learn incrementally and adjust to concept drift have gained prominence. These models can update themselves in real-time without full retraining, making them better suited for timely healthcare applications where quick, accurate predictions are critical.

This study investigates several adaptive models, such as Hoeffding Trees, Adaptive Random Forests, and Dynamic Weighted Majority (DWM), using streaming healthcare datasets. The results show that adaptive methods, particularly Adaptive Random Forests, outperform traditional static models in accuracy, adaptability, and computational efficiency. These approaches enhance predictive analytics for disease monitoring and risk detection and have strong potential for integration into clinical decision support systems and real-time patient monitoring.

The research highlights the need for adaptive learning to maintain model relevance in evolving healthcare environments and supports the deployment of intelligent, scalable, and responsive healthcare analytics platforms.

Conclusion

This study explored and evaluated adaptive data mining approaches to enhance the efficiency and accuracy of predictive analytics in the healthcare domain. Through comprehensive experimentation on real-time and streaming healthcare datasets, models such as Adaptive Random Forest, Hoeffding Tree, Dynamic Weighted Majority, and Incremental Naive Bayes were assessed in terms of their adaptability, accuracy, and resource efficiency. The results affirm that adaptive models, particularly those with built-in drift detection and ensemble capabilities, significantly outperform static counterparts in dynamic clinical environments. Adaptive Random Forest emerged as the most effective model, balancing high predictive accuracy with robustness against concept drift. These findings underscore the importance of incorporating adaptive learning mechanisms into healthcare analytics systems, especially for applications requiring real-time decision-making, such as patient monitoring, early diagnosis, and emergency response. Future work should address the integration of adaptive models with hospital EHR systems, ensure patient data privacy, and explore hybrid approaches combining domain knowledge with automated learning.

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Copyright

Copyright © 2025 Ashi Kushwah, Akash Chourasia, Anoop Vishwakarma, Harsh Bardhan Singh, Dr. Abdul Alim, Mr. Harendra Singh, Dr. Sureshwati . 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.