Potential of Reducing Crop Insurance Subsidy Based on Willingness to Pay and Random Forest Analysis

2021 ◽  
pp. 27-32
Author(s):  
Rahma Anisa ◽  
Dian Kusumaningrum ◽  
Valantino Agus Sutomo ◽  
Ken Seng Tan
Keyword(s):  
Random Forest ◽  
Willingness To Pay ◽  
Crop Insurance ◽  
Random Forest Analysis

Effect of aggregate properties on asphalt pavement friction based on random forest analysis

2021 ◽  
Vol 292 ◽  
pp. 123467
Author(s):  
You Zhan ◽  
Joshua Qiang Li ◽  
Cheng Liu ◽  
Kelvin C.P. Wang ◽  
Dominique M. Pittenger ◽  
...  
Keyword(s):  
Random Forest ◽  
Asphalt Pavement ◽  
Random Forest Analysis ◽  
Aggregate Properties ◽  
Pavement Friction

scholarly journals Random forest analysis in identifying the importance of obesity risk factors

2013 ◽  
Vol 23 (suppl_1) ◽  
Author(s):  
N Kanerva ◽  
M Erkkola ◽  
J Nevalainen ◽  
S Männistö
Keyword(s):  
Risk Factors ◽  
Random Forest ◽  
Obesity Risk ◽  
Random Forest Analysis ◽  
Obesity Risk Factors

Random forest analysis of tracheal breathing sounds for predicting obstructive sleep apnea

2019 ◽  
Vol 64 ◽  
pp. S142-S143
Author(s):  
F. Hajipour ◽  
M. Jafari Jozani ◽  
Z. Moussavi
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Random Forest ◽  
Random Forest Analysis ◽  
Obstructive Sleep

Use of random forest analysis to quantify the importance of the structural characteristics of beta-glucans for prebiotic development

2020 ◽  
Vol 108 ◽  
pp. 106001 ◽  
Author(s):  
Ka-Lung Lam ◽  
Wai-Yin Cheng ◽  
Yuting Su ◽  
Xiaojie Li ◽  
Xiyang Wu ◽  
...  
Keyword(s):  
Random Forest ◽  
Structural Characteristics ◽  
Random Forest Analysis ◽  
Beta Glucans

scholarly journals To Predict the Requirement of Pharmacotherapy by OGTT Glucose Levels in Women with GDM Classified by the IADPSG Criteria

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Gülen Yerlikaya ◽  
Veronica Falcone ◽  
Tina Stopp ◽  
Martina Mittlböck ◽  
Andrea Tura ◽  
...  
Keyword(s):  
Risk Factors ◽  
Random Forest ◽  
Risk Stratification ◽  
Predictive Accuracy ◽  
Lifestyle Modification ◽  
Clinical Risk Factors ◽  
Random Forest Analysis ◽  
Glucose Levels ◽  
Clinical Risk ◽  
Iadpsg Criteria

The aim of this study was to assess the association between OGTT glucose levels and requirement of pharmacotherapy in GDM patients classified by the IADPSG criteria. This study included 203 GDM patients (108 managed with lifestyle modification and 95 requiring pharmacotherapy). Clinical risk factors and OGTT glucose concentrations at 0 (G0), 60 (G60), and 120 min (G120) were collected. OGTT glucose levels were significantly associated with the later requirement of pharmacotherapy (ROC-AUC: 71.1, 95% CI: 63.8–78.3). Also, the combination of clinical risk factors (age, BMI, parity, and pharmacotherapy in previous gestation) showed an acceptable predictive accuracy (ROC-AUC: 72.1, 95% CI: 65.0–79.2), which was further improved when glycemic parameters were added (ROC-AUC: 77.5, 95% CI: 71.5–83.9). Random forest analysis revealed the highest variable importance for G0, G60, and age. OGTT glucose measures in addition to clinical risk factors showed promising properties for risk stratification in GDM patients classified by the recently established IADPSG criteria.

Sign in / Sign up

Export Citation Format

Share Document