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CLUSTERING AND CLASSIFICATION OF DISEASES USING STOCHASTIC DYNAMIC OPTIMIZATION

https://doi.org/10.53360/2788-7995-2025-2(18)-3

Abstract

This study presents a new approach to the optimization of Natural Language Processing (NLP) techniques for medical entity recognition and disease classification. By leveraging patient queries and PubMed article abstracts, the research uses advanced extraction methods to identify biomedical entities and diseases from medical texts. Diseases are grouped using a combination of TF-IDF and K-means clustering, and classification models are then applied to predict disease clusters based on known entities. A key innovation of this work is the use of Stochastic Dynamic Optimization to fine-tune parameters, significantly enhancing clustering and classification performance.

Experimental results demonstrate that the proposed method improves the accuracy of extraction and classification, outperforming traditional methods in terms of precision and scalability. This scalable and efficient approach to biomedical data analysis has the potential to support future clinical decision-making, enable personalized medicine, and provide valuable healthcare insights, ultimately contributing to improved patient outcomes and more effective research workflows.

About the Authors

A. Amantay
Kazakh-British Technical University
Kazakhstan

Aigerim Amantay – master’s student,

050000 Almaty, Tole bi street, 59



Zh. Makhambetali
Kazakh-British Technical University
Kazakhstan

Zhandos Makhambetali – master’s student,

050000 Almaty, Tole bi street, 59



References

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Review

For citations:


Amantay A., Makhambetali Zh. CLUSTERING AND CLASSIFICATION OF DISEASES USING STOCHASTIC DYNAMIC OPTIMIZATION. Bulletin of Shakarim University. Technical Sciences. 2025;(2(18)):23-30. https://doi.org/10.53360/2788-7995-2025-2(18)-3

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ISSN 2788-7995 (Print)
ISSN 3006-0524 (Online)
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