Lawsuit lead time prediction: Comparison of data mining techniques based on categorical response variable

• Published in: PloS one Vol. 13; no. 6; p. e0198122
• Main Authors: Gruginskie, Lúcia Adriana dos Santos, Vaccaro, Guilherme Luís Roehe
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2018; Public Library of Science (PLoS)
• Subjects: Analysis; Artificial intelligence; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Brazil; Comparative analysis; Computer and Information Sciences; Criminal Law - statistics & numerical data; Data mining; Data Mining - methods; Data processing; Databases, Factual; Engineering and Technology; Human rights; Humans; Judicial system; Judiciary; Jurisprudence; Lead time; Litigation; Mathematical models; Model accuracy; Neural networks; Neural Networks (Computer); People and places; Physical Sciences; Prediction models; Regression Analysis; Research and Analysis Methods; Social Sciences; Support Vector Machine; Support vector machines; Time Factors; Variables; Brazil; Europe; New York; Rio Grande do Sul Brazil; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0198122

The quality of the judicial system of a country can be verified by the overall length time of lawsuits, or the lead time. When the lead time is excessive, a country's economy can be affected, leading to the adoption of measures such as the creation of the Saturn Center in Europe. Although there are performance indicators to measure the lead time of lawsuits, the analysis and the fit of prediction models are still underdeveloped themes in the literature. To contribute to this subject, this article compares different prediction models according to their accuracy, sensitivity, specificity, precision, and F1 measure. The database used was from TRF4-the Tribunal Regional Federal da 4a Região-a federal court in southern Brazil, corresponding to the 2nd Instance civil lawsuits completed in 2016. The models were fitted using support vector machine, naive Bayes, random forests, and neural network approaches with categorical predictor variables. The lead time of the 2nd Instance judgment was selected as the response variable measured in days and categorized in bands. The comparison among the models showed that the support vector machine and random forest approaches produced measurements that were superior to those of the other models. The evaluation of the models was made using k-fold cross-validation similar to that applied to the test models.