Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods

Abstract

Laboratory data acquisition and analysis of X-ray diffraction (XRD) data involves a lot of tedious human engineering and is time-consuming. To put it in context, a summation of the material synthesis procedure leading to the analysis of the structure of the material can span several days. To curb this challenge and to enhance innovations in engineering pedagogy, this article investigates an alternative method that uses supervised learning algorithms based on ensemble techniques and a generalized linear model (GLM) for predicting reflection intensity (XRD patterns) of raw and natural hydroxyapatite under varying sinter ing temperature conditions given Bragg angles as input to the machine learning algorithms. For the experiment, we trained GLM and ensemble learning models (CatBoost, LightGBM, and two variants of XGBoost based on manual and genetic algorithms for tuning the hyperparameters). The results show that most instances of the XGBoost yielded a robust performance that surpasses all other approaches when predicting X-ray reflection intensities ascribed to the biomaterials subjected to varying sintering temperature conditions. In addition, the results show that all the ensemble techniques significantly outperform the GLM indicates that the former exhibits better generalization capacity. The ensemble learning techniques and the GLM present a reduced computational complexity.