TY  - EJOU
AU  - SURYAWANSHI, ABHIJEET 
AU  - BEHERA, NIRANJANA 

TI  - Application of Machine Learning For Prediction Dental Material Wear
T2  - Journal of Polymer Materials

PY  - 2023
VL  - 40
IS  - 3-4
SN  - 0976-3449

AB  - Resin composites are commonly applied as the material for dental restoration. Wear of these
materials is a major issue. In this study specimens made of dental composite materials were
subjected to an in-vitro test in a pin-on-disc tribometer. Four different dental composite materials
applied in the experiment were soaked in a solution of chewing tobacco for certain days before
being removed and put through a wear test. Subsequently, four different machine learning (ML)
algorithms (AdaBoost, CatBoost, Gradient Boosting, Random Forest) were implemented for
developing models for the prediction of wear of dental materials. AdaBoost, CatBoost, Gradient
Boosting and Random Forest model show an MAE of 0.7011, 0.0773, 0.0771 and 0.2199.
AdaBoost model performs poorly in comparison to other models.
KW  - Machine learning
KW  -  Ada Boost
KW  -  Cat Boost
KW  -  Gradient Boosting
KW  -  Random Forest

DO  - 10.32381/JPM.2023.40.3-4.11