Supplementary Appendices for: Enhanced LightGBM Model for Fetal Health Classification

This repository contains the supplementary appendices (A, B, and C) associated with the article titled "Enhanced Light-GBM Model using Borderline-SMOTE and Modified Bayesian Optimization for Early Detection of Fetal Health Classification" The appendices include: Appendix A: Algorithmic details and mathematical formulation of the Borderline-SMOTE technique. Appendix B: LightGBM training algorithm and pseudocode. Appendix C: Detailed explanation and pseudocode of Bayesian Optimization using the Tree-structured Parzen Estimator (TPE). Python Code: Complete source code for data preprocessing, model training, hyperparameter tuning, and evaluation to support full reproducibility. These materials provide additional technical depth to support reproducibility and transparency of the proposed methodology. Readers are encouraged to consult these appendices for further insights into the model development, preprocessing strategies, and optimization procedures used in the study.