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

Area of Science:

• Computational Social Science
• Legal Informatics
• Machine Learning Applications

Background:

• Judicial system efficiency is crucial for economic stability, with excessive lawsuit lead times negatively impacting national economies.
• While performance indicators exist for lawsuit duration, predictive modeling for lead time analysis remains underdeveloped in legal informatics research.
• The creation of specialized centers, like Europe's Saturn Center, highlights efforts to manage and reduce judicial processing times.

Purpose of the Study:

• To compare the predictive accuracy of various machine learning models for estimating lawsuit lead times.
• To evaluate model performance using metrics such as accuracy, sensitivity, specificity, precision, and F1 score.
• To identify the most effective predictive models for analyzing and forecasting judicial process duration.

Main Methods:

• Utilized a dataset of 2nd Instance civil lawsuits completed in 2016 from Brazil's TRF4 (Tribunal Regional Federal da 4a Região).
• Fitted predictive models including support vector machine, naive Bayes, random forests, and neural networks using categorical predictor variables.
• Treated lawsuit lead time in days as the response variable, categorized into distinct bands for analysis.

Main Results:

• Support vector machine and random forest models exhibited superior predictive performance compared to naive Bayes and neural network approaches.
• The evaluation employed k-fold cross-validation, a robust method for assessing model generalization and reliability.
• Specific performance metrics (accuracy, sensitivity, specificity, precision, F1 measure) were used to quantitatively compare the models.

Conclusions:

• Support vector machines and random forests are highly effective for predicting lawsuit lead times in judicial systems.
• Accurate prediction of judicial process duration can inform policy and resource allocation to improve legal system efficiency.
• Further research into advanced predictive modeling can enhance the analysis of legal process performance indicators.