APPLICATION OF HYBRID MACHINE LEARNING METHODS FOR DIABETES DIAGNOSIS

Authors

  • Дилноз Мухамедиева National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Uzbekistan
  • Дильфуза Васиева National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Uzbekistan
  • Рузимбой Собиров National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Uzbekistan
  • Ахлиддин Нажмиддинов National Research University "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers", Uzbekistan

Keywords:

machine learning, random forest, gradient boosting, stacking model, metric.

Abstract

The paper explores the use of ensemble methods such as random forest, gradient boosting, and bеgging for diagnosing diabetes mellitus and analyzes their advantages and challenges. Hybrid methods help to increase diagnostic accuracy and reduce false positives and false negatives. Overall, hybrid machine learning techniques represent a promising tool for improving diabetes diagnosis and may contribute to more effective detection and management of this chronic disease.

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Published

2024-07-20

Issue

Vol. 1 No. 7 (2024): “YANGI O‘ZBEKISTONNING TARAQQIYOT DAVRIDA FAN, TA’LIM VA INNOVATSION TEXNOLOGIYALARNI RIVOJLANTIRISHNING DOLZARB MASALALARI”

Section

Articles

License

Copyright (c) 2024 Дилноз Мухамедиева, Дильфуза Васиева, Рузимбой Собиров, Ахлиддин Нажмиддинов

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

APPLICATION OF HYBRID MACHINE LEARNING METHODS FOR DIABETES DIAGNOSIS. (2024). Universal International Scientific Journal, 1(7), 58-64. https://universaljurnal.uz/index.php/jurnal/article/view/873