Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Gruginskie LADS

PROVIDER: S-EPMC5983432 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Gruginskie Lúcia Adriana Dos Santos LADS Vaccaro Guilherme Luís Roehe GLR

PloS one 20180601 6

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 articl ...[more]

PMID: 29856787

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Project description:BACKGROUND:About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques are tools that can improve the analysis and interpretation or extraction of knowledge from the data. These techniques may enhance the prognosis and diagnosis associated with reducing diseases such as T2DM. We applied four classification models, including K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and artificial neural networks (ANN) for diagnosing T2DM, and we compared the diagnostic power of these algorithms with each other. We performed the algorithms on six LncRNA variables (LINC00523, LINC00995, HCG27_201, TPT1-AS1, LY86-AS1, DKFZP) and demographic data. RESULTS:To select the best performance, we considered the AUC, sensitivity, specificity, plotted the ROC curve, and showed the average curve and range. The mean AUC for the KNN algorithm was 91% with 0.09 standard deviation (SD); the mean sensitivity and specificity were 96 and 85%, respectively. After applying the SVM algorithm, the mean AUC obtained 95% after stratified 10-fold cross-validation, and the SD obtained 0.05. The mean sensitivity and specificity were 95 and 86%, respectively. The mean AUC for ANN and the SD were 93% and 0.03, also the mean sensitivity and specificity were 78 and 85%. At last, for the logistic regression algorithm, our results showed 95% of mean AUC, and the SD of 0.05, the mean sensitivity and specificity were 92 and 85%, respectively. According to the ROCs, the Logistic Regression and SVM had a better area under the curve compared to the others. CONCLUSION:We aimed to find the best data mining approach for the prediction of T2DM using six lncRNA expression. According to the finding, the maximum AUC dedicated to SVM and logistic regression, among others, KNN and ANN also had the high mean AUC and small standard deviations of AUC scores among the approaches, KNN had the highest mean sensitivity and the highest specificity belonged to SVM. This study's result could improve our knowledge about the early detection and diagnosis of T2DM using the lncRNAs as biomarkers.

Project description:BackgroundHyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.MethodsThis study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa--EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia.ResultsThe application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic.ConclusionsThe findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

Dataset Information

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

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

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