scholarly journals Statistical Analysis of Treatment Planning Parameters for Prediction of Delivery Quality Assurance Failure for Helical Tomotherapy

2020 ◽  
Vol 19 ◽  
pp. 153303382097969
Author(s):  
Kyung Hwan Chang ◽  
Young Hyun Lee ◽  
Byung Hun Park ◽  
Min Cheol Han ◽  
Jihun Kim ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Treatment Planning ◽  
Planning Process ◽  
Helical Tomotherapy ◽  
Regression Tree ◽  
Roc Curves ◽  
Classification And Regression Tree ◽  
Variable Model ◽  
Predicted Probability ◽  
Cart Algorithm

Purpose: This study aimed to investigate the parameters with a significant impact on delivery quality assurance (DQA) failure and analyze the planning parameters as possible predictors of DQA failure for helical tomotherapy. Methods: In total, 212 patients who passed or failed DQA measurements were retrospectively included in this study. Brain (n = 43), head and neck (n = 37), spinal (n = 12), prostate (n = 36), rectal (n = 36), pelvis (n = 13), cranial spinal irradiation and a treatment field including lymph nodes (n = 24), and other types of cancer (n = 11) were selected. The correlation between DQA results and treatment planning parameters were analyzed using logistic regression analysis. Receiver operating characteristic (ROC) curves, areas under the curves (AUCs), and the Classification and Regression Tree (CART) algorithm were used to analyze treatment planning parameters as possible predictors for DQA failure. Results: The AUC for leaf open time (LOT) was 0.70, and its cut-off point was approximately 30%. The ROC curve for the predicted probability calculated when the multivariate variable model was applied showed an AUC of 0.815. We confirmed that total monitor units, total dose, and LOT were significant predictors for DQA failure using the CART. Conclusions: The probability of DQA failure was higher when the percentage of LOT below 100 ms was higher than 30%. The percentage of LOT below 100 ms should be considered in the treatment planning process. The findings from this study may assist in the prediction of DQA failure in the future.

scholarly journals Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1405 ◽  
Author(s):  
Seyed Naghibi ◽  
Mehdi Vafakhah ◽  
Hossein Hashemi ◽  
Biswajeet Pradhan ◽  
Seyed Alavi
Keyword(s):  
Data Mining ◽  
Regression Tree ◽  
Roc Curves ◽  
Flood Damage ◽  
Boosted Regression Trees ◽  
Classification And Regression Tree ◽  
Operating Characteristics ◽  
National Guidelines ◽  
Conditioning Factors ◽  
Suitability Maps

It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions.

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

scholarly journals Is age an additional factor in the treatment of elderly patients with glioblastoma? A new stratification model: an Italian Multicenter Study

2020 ◽  
Vol 49 (4) ◽  
pp. E13 ◽  
Author(s):  
Tamara Ius ◽  
Teresa Somma ◽  
Roberto Altieri ◽  
Filippo Flavio Angileri ◽  
Giuseppe Maria Barbagallo ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Therapeutic Option ◽  
Regression Tree ◽  
Continuous Variable ◽  
Classification And Regression Tree ◽  
Imaging Data ◽  
Cart Analysis ◽  
Survival Score ◽  
Molecular Features ◽  
Cart Algorithm

OBJECTIVEApproximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients.METHODSClinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score.RESULTSSurvival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell’s c-index of 0.75 (95% CI 0.76–0.81).CONCLUSIONSThe CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly GBM patients with different surgical, radiological, and molecular profiles, thus assisting physicians in daily clinical management. The preliminary model, however, requires validation with future prospective investigations. Practical recommendations for clinicians/surgeons would strengthen the quality of the study; e.g., surgery can be considered as a first therapeutic option in the workflow of elderly patients with GBM, especially when the preoperative estimated EOR is greater than 80%.

scholarly journals Characterization of municipalities with high road traffic fatality rates using macro level data and the CART algorithm

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Cintia Isabel De Campos ◽  
Murilo Castanho Dos Santos ◽  
Cira Souza Pitombo
Keyword(s):  
Traffic Accidents ◽  
Injury Severity ◽  
Road Traffic ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Macro Level ◽  
Traffic Fatality ◽  
Level Data ◽  
The Government ◽  
Cart Algorithm

Road traffic accidents occur daily caused by different factors leading to varying degrees of injury severity. Considering this, many studies have been developed to identify and understand these factors to implement preventive actions. A Decision Tree (DT) is one of the techniques that can generate classifications and predictions by detecting a priori unknown patterns. This study aims to identify the characteristics of municipalities with high and very high fatality rates caused by traffic accidents, using a macro level dataset and a DT algorithm (CART - Classification And Regression Tree). Therefore, macro level data from the municipalities of São Paulo state (Brazil) were used, such as demographic and socioeconomic data, fatality rates and other variables related to traffic. The results indicated the Gross Domestic Product (GDP) as the most important variable, and the municipalities were characterized mainly considering the size of the highway network and vehicle fleet (trucks, minibuses, cars, motorcycles). These characteristics could provide support to the government to plan mitigating actions in municipalities with the highest tendency to high traffic fatality rates.

Abstract 12302: Mortality and Readmissions After Heart Failure Hospitalization in a Community Based Cohort: Estimating Risk Using the Acute Decompensated Heart Failure National Registry (ADHERE) Classification and Regression Tree (CART) Algorithm

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Sithu Win ◽  
Imad Hussain ◽  
Virginia Hebl ◽  
Margaret M Redfield
Keyword(s):  
Heart Failure ◽  
Acute Decompensated Heart Failure ◽  
Regression Tree ◽  
Decompensated Heart Failure ◽  
National Registry ◽  
Classification And Regression Tree ◽  
Post Discharge ◽  
Community Based ◽  
Cart Algorithm ◽  
Risk Categories

Background: The Acute Decompensated Heart Failure National Registry (ADHERE) Classification and Regression Tree (CART) algorithm is an accepted method to assess a heart failure (HF) patient’s risk of inpatient mortality based on the patient’s systolic blood pressure (≥115 vs. <115 mmHg), blood urea nitrogen (BUN; ≥43 vs. <43 mg/dL), and creatinine (≥2.75 vs. <2.75 mg/dL) at the time of admission. Whether the ADHERE CART algorithm identifies risk of longer term poor outcomes and is predictive in patients with systolic (EF < 50%) or diastolic (EF ≥ 50%) HF in the community is unclear. Methods: We identified all hospitalizations for a primary diagnosis of HF occurring between 2000-2013 in a community-based cohort living within 40 miles of Rochester, MN. Outcomes including length of stay, in-hospital mortality, 30 and 90 day post-discharge mortality and readmission for any cause at 30 and 90 days were compared across the ADHERE CART risk groups using logistic regression for the entire cohort and separately for systolic (EF < 50%) and diastolic (EF ≥ 50%) HF. Results: See Table. We examined 5,918 heart failure hospitalizations among 3,628 individual patients. Distribution of hospitalizations across the risk categories is shown in table. Length of stay, in-hospital mortality, 30 and 90 day mortality and 30 and 90 day readmission rates all increased with increasing ADHERE risk categories. Similar results were obtained separately for systolic and diastolic HF. Conclusion: The ADHERE CART algorithm is simple, uses data universally available on admission, and identifies groups that differ substantially in their post-discharge adverse outcomes regardless of HF type (systolic vs diastolic) in the community. The ADHERE CART algorithm may inform clinical decision regarding advanced HF treatments, end-of-life planning and care transition services in patients hospitalized for HF in the community.

Mutual information-based multi-output tree learning algorithm

2021 ◽  
Vol 25 (6) ◽  
pp. 1525-1545
Author(s):  
Hyun-Seok Kang ◽  
Chi-Hyuck Jun
Keyword(s):  
Mutual Information ◽  
Variable Selection ◽  
Time Complexity ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Tree Model ◽  
Industrial Systems ◽  
Output Dimension ◽  
Cart Algorithm

A tree model with low time complexity can support the application of artificial intelligence to industrial systems. Variable selection based tree learning algorithms are more time efficient than existing Classification and Regression Tree (CART) algorithms. To our best knowledge, there is no attempt to deal with categorical input variable in variable selection based multi-output tree learning. Also, in the case of multi-output regression tree, a conventional variable selection based algorithm is not suitable to large datasets. We propose a mutual information-based multi-output tree learning algorithm that consists of variable selection and split optimization. The proposed method discretizes each variable based on k-means into 2–4 clusters and selects the variable for splitting based on the discretized variables using mutual information. This variable selection component has relatively low time complexity and can be applied regardless of output dimension and types. The proposed split optimization component is more efficient than an exhaustive search. The performance of the proposed tree learning algorithm is similar to or better than that of a multi-output version of CART algorithm on a specific dataset. In addition, with a large dataset, the time complexity of the proposed algorithm is significantly reduced compared to a CART algorithm.

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