XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis
Cervical cancer continues to pose a significant global health challenge, with early detection remaining the cornerstone for effective intervention. This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms...
Saved in:
Main Authors: | Sudi Suryadi, Masrizal |
---|---|
Format: | Article |
Language: | English |
Published: |
Ikatan Ahli Informatika Indonesia
2025-06-01
|
Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
Subjects: | |
Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6587 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Clinical and economic evaluation of the effectiveness of modern strategies for screening and early diagnosis of cervical cancer in the Russian Federation
by: Zh. V. Khaylova, et al.
Published: (2025-05-01) -
Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization
by: Syaiful Anam, et al.
Published: (2025-06-01) -
Model Prediksi Risiko Kesehatan Perkotaan Berbasis Lingkungan dengan XGBoost
by: Muhammad Kahfi Aulia, et al.
Published: (2025-07-01) -
Rekomendasi Pemilihan Jenis Tanaman Menggunakan Algoritma Random Forest dan XGBoost Regressor
by: Abdul Rahman, et al.
Published: (2024-07-01) -
Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms
by: Jose Herrera‐Camacho, et al.
Published: (2025-07-01)