scholarly journals OC19.07: Decision-tree analysis incorporating hCG levels versus risk prediction model (M4): prospective interventional study in the management of PUL

2014 ◽  
Vol 44 (S1) ◽  
pp. 45-45
Author(s):  
F. Infante ◽  
C. Lu ◽  
U. Menakaya ◽  
G. Condous
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Decision Tree Analysis ◽  
Interventional Study ◽  
Tree Analysis ◽  
Versus Risk

scholarly journals A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Daichi Shigemizu ◽  
Shintaro Akiyama ◽  
Yuya Asanomi ◽  
Keith A. Boroevich ◽  
Alok Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Risk Prediction ◽  
Mirna Expression ◽  
Dementia With Lewy Bodies ◽  
Target Genes ◽  
Lewy Bodies ◽  
Risk Prediction Model ◽  
Gradient Boosting

Abstract Background Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer’s disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential biomarkers of prodromal DLB is still challenging. MicroRNAs (miRNAs) have recently received a lot of attention as a source of novel biomarkers. Methods In this study, using serum miRNA expression of 478 Japanese individuals, we investigated potential miRNA biomarkers and constructed an optimal risk prediction model based on several machine learning methods: penalized regression, random forest, support vector machine, and gradient boosting decision tree. Results The final risk prediction model, constructed via a gradient boosting decision tree using 180 miRNAs and two clinical features, achieved an accuracy of 0.829 on an independent test set. We further predicted candidate target genes from the miRNAs. Gene set enrichment analysis of the miRNA target genes revealed 6 functional genes included in the DHA signaling pathway associated with DLB pathology. Two of them were further supported by gene-based association studies using a large number of single nucleotide polymorphism markers (BCL2L1: P = 0.012, PIK3R2: P = 0.021). Conclusions Our proposed prediction model provides an effective tool for DLB classification. Also, a gene-based association test of rare variants revealed that BCL2L1 and PIK3R2 were statistically significantly associated with DLB.

scholarly journals A Novel Clinical Prediction Model for Prognosis in Malignant Pleural Mesothelioma Using Decision Tree Analysis

2016 ◽  
Vol 11 (4) ◽  
pp. 573-582 ◽  
Author(s):  
Fraser J.H. Brims ◽  
Tarek M. Meniawy ◽  
Ian Duffus ◽  
Duneesha de Fonseka ◽  
Amanda Segal ◽  
...  
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Malignant Pleural Mesothelioma ◽  
Pleural Mesothelioma ◽  
Decision Tree Analysis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Tree Analysis

Prediction model based on decision tree analysis for laccase mediators

2013 ◽  
Vol 52 (1) ◽  
pp. 68-76 ◽  
Author(s):  
Fabiola Medina ◽  
Sergio Aguila ◽  
Maria Camilla Baratto ◽  
Andrea Martorana ◽  
Riccardo Basosi ◽  
...  
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Model Based

scholarly journals Predicting Real-Time Crash Risk for Urban Expressways in China

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Miaomiao Liu ◽  
Yongsheng Chen
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Fuzzy Inference ◽  
Risk Prediction Model ◽  
Training Data ◽  
Sensor Data ◽  
Crash Risk ◽  
Inference System

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

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