GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION

  • Shafa Fitria Aqilah Khansa Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel, Indonesia https://orcid.org/0009-0005-7377-9582
  • Nurissaidah Ulinnuha Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel, Indonesia https://orcid.org/0000-0003-4127-2590
  • Wika Dianita Utami Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel, Indonesia https://orcid.org/0009-0004-9214-9845
Keywords: Classification, Cross Validation, Hyperparameter Tuning, Parkinson, XGBoost

Abstract

Parkinson's disease is a neurodegenerative disorder affecting motor abilities, with a prevalence of 329 cases per 100,000 individuals. Early diagnosis is crucial to prevent complications. This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. The XGBoost method effectively manages large and complex data and reduces. Tuning was performed with 5-fold cross-validation for result validity. After tuning with Grid Search, the model achieved 93.35% accuracy in 44 minutes 51 seconds, with optimal parameters gamma=5, max depth=3, learning rate=0.3, n estimators=100, and subsample=0.7. Meanwhile, Random Search with 50 iterations achieved 93.97% accuracy in 3 minutes 4 seconds with optimal parameters gamma=5, max depth=3, learning rate=0.262, n estimators=58, and subsample=0.631. Random Search also shows better time efficiency than Grid Search, although with relatively similar accuracy. The results of this study confirm that hyperparameter tuning using Random Search not only produces competitive accuracy performance but also minimizes computation time, making it a more optimal choice for Parkinson's disease classification.

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Published
2025-07-01
How to Cite
[1]
S. F. Aqilah Khansa, N. Ulinnuha, and W. D. Utami, “GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 1609-1624, Jul. 2025.