Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction

Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction

Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatr...

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Bibliographic Details
Main Authors: Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2025-06-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
pediatric spinal surgery
post-operative kyphosis
machine learning
decision trees
imbalanced classification
Online Access:https://jurnal.iaii.or.id/index.php/RESTI/article/view/6606
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