An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: Classification and regression tree (CART) analysis

2020 ◽  
Vol 127 ◽  
pp. 109012 ◽  
Author(s):  
Eun Jung Shim ◽  
Min A Yoon ◽  
Hye Jin Yoo ◽  
Choong Guen Chee ◽  
Min Hee Lee ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Well Differentiated

scholarly journals A diagnostic strategy for Parkinsonian syndromes using quantitative indices of DAT SPECT and MIBG scintigraphy: an investigation using the classification and regression tree analysis

2021 ◽  
Author(s):  
Yu Iwabuchi ◽  
Masashi Kameyama ◽  
Yohji Matsusaka ◽  
Hidetoshi Narimatsu ◽  
Masahiro Hashimoto ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Mibg Scintigraphy ◽  
Tree Model ◽  
Quantitative Indices ◽  
Cart Analysis ◽  
Parkinsonian Syndromes ◽  
Dat Spect ◽  
Classification And Regression

Abstract Purpose We aimed to evaluate the diagnostic performances of quantitative indices obtained from dopamine transporter (DAT) single-photon emission computed tomography (SPECT) and 123I-metaiodobenzylguanidine (MIBG) scintigraphy for Parkinsonian syndromes (PS) using the classification and regression tree (CART) analysis. Methods We retrospectively enrolled 216 patients with or without PS, including 80 without PS (NPS) and 136 with PS [90 Parkinson’s disease (PD), 21 dementia with Lewy bodies (DLB), 16 progressive supranuclear palsy (PSP), and 9 multiple system atrophy (MSA). The striatal binding ratio (SBR), putamen-to-caudate ratio (PCR), and asymmetry index (AI) were calculated using DAT SPECT. The heart-to-mediastinum uptake ratio (H/M) based on the early (H/M [Early]) and delayed (H/M [Delay]) images and cardiac washout rate (WR) were calculated from MIBG scintigraphy. The CART analysis was used to establish a diagnostic decision tree model for differentiating PS based on these quantitative indices. Results The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 87.5, 96.3, 93.3, 92.9, and 93.1 for NPS; 91.1, 78.6, 75.2, 92.5, and 83.8 for PD; 57.1, 95.9, 60.0, 95.4, and 92.1 for DLB; and 50.0, 98.0, 66.7, 96.1, and 94.4 for PSP, respectively. The PCR, WR, H/M (Delay), and SBR indices played important roles in the optimal decision tree model, and their feature importance was 0.61, 0.22, 0.11, and 0.05, respectively. Conclusion The quantitative indices showed high diagnostic performances in differentiating NPS, PD, DLB, and PSP, but not MSA. Our findings provide useful guidance on how to apply these quantitative indices in clinical practice.

Classification and regression tree (CART) analysis for indicator bacterial concentration prediction for a Californian coastal area

2010 ◽  
Vol 61 (2) ◽  
pp. 545-553 ◽  
Author(s):  
Hun-Kyun Bae ◽  
Betty H. Olson ◽  
Kuo-Lin Hsu ◽  
Soroosh Sorooshian
Keyword(s):  
Decision Tree ◽  
Fecal Coliform ◽  
Mean Squared Error ◽  
Regression Tree ◽  
Total Coliform ◽  
Classification And Regression Tree ◽  
Good Representation ◽  
Tree Structures ◽  
Cart Analysis ◽  
Classification And Regression

The study used existing indicator bacterial data and a number of physicochemical parameters that can be measured instantaneously to determine if a decision tree approach, especially classification and regression tree, could be used to predict bacterial concentrations in timely manner for beach closure management. Each indicator bacteria showed different tree structures and each had its own significant variables; Dissolved oxygen played an important role for both total coliform and fecal coliform and turbidity was the most important factor to predict concentrations of enterococci for decision tree approaches. Root mean squared error stayed between 5 and 6.5% of the average values of observations; RMSEs from each simulation, 0.25 for total coliform, 0.31 for fecal coliform, and 0.29 for enterococci. Estimations from tree structures would be regarded as a good representation of the actual data. In addition to results of the objective function, RMSE, 77.5% of actual value fell into the 95% of confidence interval of estimations for total coliform concentrations, 60% for fecal coliform concentrations, and 62.5% for enterococci concentrations. The approach showed reliable estimations for the majority of the data processed, although the method did not portray low concentrations of bacteria as well.

scholarly journals Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaizhou Huang ◽  
Feiyang Ji ◽  
Zhongyang Xie ◽  
Daxian Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Hepatitis B ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Artificial Liver ◽  
Liver Support ◽  
Artificial Liver Support ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Cart Model

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

scholarly journals Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

Archaea ◽  
2008 ◽  
Vol 2 (3) ◽  
pp. 159-167 ◽  
Author(s):  
Betsey Dexter Dyer ◽  
Michael J. Kahn ◽  
Mark D. LeBlanc
Keyword(s):  
Environmental Conditions ◽  
Convergent Evolution ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Evolutionary Divergence ◽  
Cart Analysis ◽  
Genomic Signatures ◽  
Genome Wide ◽  
Classification And Regression ◽  
Temperature Optima

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results.

scholarly journals Analisis Kecenderungan Keterlambatan Pembayaran Pengecekan Kapal di Pelabuhan Regional Riau

2019 ◽  
Vol 2 (2) ◽  
pp. 92-98
Author(s):  
Hespri Yomeldi ◽  
Moh Roufiq Azmy ◽  
Ryche Pranita
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Ministry Of Health ◽  
Tree Algorithm ◽  
Health Checks ◽  
Depth Study ◽  
Classification And Regression ◽  
Port Health

Ship health checks must be carried out which function to provide a sailing permit. The implementation of ship health checks is carried out in collaboration with the ministry of health and transportation. The implementation of the activity, commonly known as Port Health Quarantine Clearance (PHQC) requires time to check and the ship makes a payment check to be able to issue a sailing permit. The problem that arises in the field is that the ship delays PHQC payments and then impacts on the buildup of ships in the port, besides that officers also need longer time to process the issuance of sailing permits. This of course has an impact on other port services such as dwelling time and scheduled departures that can be delayed. In overcoming this problem, an in-depth study is needed to analyze the trend of late payment of ship health checks, what variables influence it and how treatment is done to overcome these problems. Using logistic regression and decision tree with Classification and Regression Tree algorithm , a model is then developed that determines the variables that affect the delay of the ship making PHQC payments.

Thresholding of prominent biomarkers of breast cancer on overall survival using classification and regression tree

2021 ◽  
pp. 1-10
Author(s):  
Pragya Kumari ◽  
Gajendra K. Vishwakarma ◽  
Atanu Bhattacharjee
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Survival Analysis ◽  
Regression Tree ◽  
Vital Role ◽  
Classification And Regression Tree ◽  
Brier Score ◽  
Risk Of Death ◽  
Cart Analysis ◽  
Classification And Regression

BACKGROUND: HER2, ER, PR, and ERBB2 play a vital role in treating breast cancer. These are significant predictive and prognosis biomarkers of breast cancer. OBJECTIVE: We aim to obtain a unique biomarker-specific prediction on overall survival to know their survival and death risk. METHODS: Survival analysis is performed on classified data using Classification and Regression Tree (CART) analysis. Hazard ratio and Confidence Interval are computed using MLE and the Bayesian approach with the CPH model for univariate and multivariable illustrations. Validation of CART is executed with the Brier score, and accuracy and sensitivity are obtained using the k-nn classifier. RESULTS: Utilizing CART analysis, the cut-off value of continuous-valued biomarkers HER2, ER, PR, and ERBB2 are obtained as 14.707, 8.128, 13.153, and 6.884, respectively. Brier score of CART is 0.16 towards validation of methodology. Survival analysis gives a demonstration of the survival estimates with significant statistical strategies. CONCLUSIONS: Patients with breast cancer are at low risk of death, whose HER2 value is below its cut-off value, and ER, PR, and ERBB2 values are greater than their cut-off values. This comparison is with the patient having the opposite side of these cut-off values for the same biomarkers.

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