scholarly journals Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

2020 ◽  
Vol 10 (2) ◽  
pp. 425 ◽  
Author(s):  
Wei Chen ◽  
Yang Li ◽  
Paraskevas Tsangaratos ◽  
Himan Shahabi ◽  
Ioanna Ilia ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Shaanxi Province ◽  
Kernel Logistic Regression ◽  
Alternating Decision Tree ◽  
Auc Value ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

scholarly journals Artificial Intelligence Approach for Variant Reporting

2018 ◽  
pp. 1-13 ◽  
Author(s):  
Michael G. Zomnir ◽  
Lev Lipkin ◽  
Maciej Pacula ◽  
Enrique Dominguez Meneses ◽  
Allison MacLeay ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Decision Support ◽  
Next Generation Sequencing ◽  
Decision Support Tool ◽  
Bioinformatics Pipeline ◽  
Support Tool ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach ◽  
Generation Sequencing

Purpose Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging. Methods We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting. Results For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models’ Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation. Conclusion Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Sign in / Sign up

Export Citation Format

Share Document