Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Engineering Reports
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.
Description
Research Article
Keywords
biomaterials, ensemble learning, generalized linear model, predictive modeling
Citation
Okafor E, Obada DO, Ibrahim Y, Dodoo-Arhin D. Prediction of the reflection intensity of natural hydroxyapatite using generalized linear model and ensemble learning methods. Engineering Reports. 2021;3:e12292. https://doi.org/10.1002/eng2.12292