Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model ...

AIMS This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. MATERIALS AND METHODS We used data from 5,646 adults aged between 18-7...

abstract level of groundwater is decreased which is mainly because, withdrawal of groundwater is increased, because of lack of right management. economics of rural based farming and farming is dependent of water so decreasing of groundwater effects social welfare. in this study decrease of welfare measure by reason of reduction of groundwater level considered. this study investigated the impact...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

OBJECTIVES To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the 'Random Forest' algorithm. DESIGN Case-control study. PARTICIPANTS 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. INTERVENTIONS Spectral doma...