Moisture content prediction model for pharmaceutical granules using
machine learning techniques
Abstract
The aim of this study was to develop a prediction model and identifying
relative important factors in the evaluation of moisture content of
pharmaceutical granules using Artificial Neural Network (ANN) and
Support Vector Machine (SVM) techniques for the data sets of Addis Pharmaceutical Factory (APF). Optimal models of ANN and SVM
models were developed and compared, utilizing Matlab16.0 as a software
tool. The performance of the models is evaluated using a quantitative
error metric; mean squared error (MSE), Regression(R) and Confusion
Matrix (CM). This study reveals that the ANN model is an optimal model
for predicting moisture content of pharmaceutical granules for the datasets
of APF. The model of ANN, with MSE of 0.016941 and classification
accuracy of 98.7% is built and accepted as optimal model for predicting
moisture content of pharmaceutical granules. Temperature, Mixer
time, Initial moisture, air flow rate and drying time respectively are
the most important factors in determining the moisture content of the
granules.