Abstract:
Hyperglycemia, characterized by elevated blood glucose levels, is a critical condition that can lead to severe health complications if not detected and managed early. This study explores the application of the Cat Boost ensemble technique for the early prediction of hyperglycemia. Cat Boost, a gradient boosting algorithm that handles categorical features efficiently, is employed to develop a predictive model using a comprehensive dataset comprising patient demographics, medical history, lifestyle factors, and genetic information. The dataset undergoes rigorous preprocessing, including data cleaning, feature engineering, and normalization. The model is trained and validated using an 80-20 train-test split and evaluated through cross-validation to ensure robustness. Key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC are utilized to assess the model's effectiveness. This study demonstrates the potential of the Cat Boost ensemble technique in the early detection of hyperglycemia, offering a valuable tool for healthcare professionals to identify at-risk individuals and implement timely interventions. The proposed model provides 86.15% in prediction of hyperglycemia.
Page(s):
1175-1181
DOI:
DOI not available
Published:
Journal: Abstract Book: 1st International Conference on Recent Advances in Zoology in the Era of Climate Change, May 04-05, 2026, Volume: 16, Issue: 4, Year: 2024
Keywords:
Hyperglycemia
,
RMSE
,
Ensemble Etc
,
MAE
,
Cat boost