Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation
We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized logistic regression, can be employed when separation occurs in a two-class classification setting, and allow for the calculation of likelihood ratios. A case...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-10-01
|
| Series: | Frontiers in Chemistry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2020.00738/full |
| _version_ | 1819000877979009024 |
|---|---|
| author | Giulia Biosa Diana Giurghita Eugenio Alladio Eugenio Alladio Marco Vincenti Marco Vincenti Tereza Neocleous |
| author_facet | Giulia Biosa Diana Giurghita Eugenio Alladio Eugenio Alladio Marco Vincenti Marco Vincenti Tereza Neocleous |
| author_sort | Giulia Biosa |
| collection | DOAJ |
| description | We demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized 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 fields of analytical and forensic chemistry, where it is a common feature to have a large number of biomarkers, and allows for flexibility in model assumptions such as multivariate normality. While some penalized regression methods have been introduced previously in forensic applications, our study is meant to encourage practitioners to use these powerful methods more widely. As such, 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. We anticipate that this open-source and free-of-charge application will provide a powerful and dynamic tool to infer the LR value in case of classification tasks. |
| first_indexed | 2024-12-20T22:40:18Z |
| format | Article |
| id | doaj.art-45f42b59a1044fe49421e174783c0463 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-20T22:40:18Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-45f42b59a1044fe49421e174783c04632022-12-21T19:24:29ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462020-10-01810.3389/fchem.2020.00738549597Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny ImplementationGiulia Biosa0Diana Giurghita1Eugenio Alladio2Eugenio Alladio3Marco Vincenti4Marco Vincenti5Tereza Neocleous6Forensic Toxicology Laboratory, Department of Health Surveillance and Bioethics, Catholic University of the Sacred Heart, F. Policlinico Gemelli IRCCS, Rome, ItalySchool of Mathematics and Statistics, University of Glasgow, Glasgow, United KingdomForensic Biology Unit, Carabinieri Scientific Investigations Department of Rome, Rome, ItalyDepartment of Chemistry, University of Turin, Turin, ItalyDepartment of Chemistry, University of Turin, Turin, ItalyAnti-doping and Toxicology Center “A. Bertinaria” of Orbassano, Turin, ItalySchool of Mathematics and Statistics, University of Glasgow, Glasgow, United KingdomWe demonstrate the use of classification methods that are well-suited for forensic toxicology applications. The methods are based on penalized 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 fields of analytical and forensic chemistry, where it is a common feature to have a large number of biomarkers, and allows for flexibility in model assumptions such as multivariate normality. While some penalized regression methods have been introduced previously in forensic applications, our study is meant to encourage practitioners to use these powerful methods more widely. As such, 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. We anticipate that this open-source and free-of-charge application will provide a powerful and dynamic tool to infer the LR value in case of classification tasks.https://www.frontiersin.org/articles/10.3389/fchem.2020.00738/fullclassificationlikelihood ratiologistic regressionseparationforensic scienceCllr |
| spellingShingle | Giulia Biosa Diana Giurghita Eugenio Alladio Eugenio Alladio Marco Vincenti Marco Vincenti Tereza Neocleous Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation Frontiers in Chemistry classification likelihood ratio logistic regression separation forensic science Cllr |
| title | Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation |
| title_full | Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation |
| title_fullStr | Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation |
| title_full_unstemmed | Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation |
| title_short | Evaluation of Forensic Data Using Logistic Regression-Based Classification Methods and an R Shiny Implementation |
| title_sort | evaluation of forensic data using logistic regression based classification methods and an r shiny implementation |
| topic | classification likelihood ratio logistic regression separation forensic science Cllr |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2020.00738/full |
| work_keys_str_mv | AT giuliabiosa evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT dianagiurghita evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT eugenioalladio evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT eugenioalladio evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT marcovincenti evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT marcovincenti evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation AT terezaneocleous evaluationofforensicdatausinglogisticregressionbasedclassificationmethodsandanrshinyimplementation |