Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation - Biosa, Giulia; Giurghita, Diana; Alladio, Eugenio; Vincenti, Marco; Neocleous, Tereza (Frontiers in Chemistry, 2020)
We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalised logistic regression, can be employed when separation occurs in a two-class classification setting, and allow for the calculation of likelihood ratios. A case study of this framework is demonstrated on alcohol biomarker data for classifying chronic alcohol drinkers. The approach can be extended to applications in the field, for instance, of analytical and forensic chemistry, with a large number of biomarkers and allows for flexibility in model assumptions such as multivariate normality. Based upon our proof-of-concept studies, we also introduce an R Shiny online tool with an intuitive interface able to perform several classification methods. This open-source and free-of-charge application may represent a powerful and dynamic tool to infer the LR value in case of classification tasks.