Michal Hrdlicka, Tomas Urbanek, Adela Rotreklova, Aneta Kultova, Ondrej Valek, Iva Dudova
Predictors of age at diagnosis in autism spectrum disorders: the use of multiple regression analyses and a classification tree on a clinical sample
European child & adolescent psychiatry
quantitative analysis glucose using spectroscopy topic great significance interest science industry one conundrum area deploying appropriate preprocessing regression tools contribute addressing challenge study conducted comprehensive novel comparative analysis various machine learning preprocessing filtering techniques applied nearinfrared midinfrared combination nearinfrared midinfrared spectroscopy glucose assay objective evaluate effectiveness techniques accurately predicting glucose levels determine approach optimal investigation involved acquisition spectral data samples glucose solutions using three aforementioned spectroscopy techniques data subjected several preprocessing filtering methods including convolutional moving average savitzkygolay multiplicative scatter correction normalisation applied representative machine learning algorithms three categories linear modelling traditional nonlinear modelling artificial neural networks evaluation results revealed linear models exhibited higher predictive accuracy nonlinear models whereas artificial neural network models demonstrated comparable performance additionally comparative analysis various filtering methods demonstrated convolutional moving average savitzkygolay filters yielded precise outcomes overall conclusion study provides valuable insights efficacy different machine learning techniques glucose measurement highlights importance applying appropriate filtering methods enhancing predictive accuracy findings important implications development new improved glucose quantification technologies