scholarly journals Serum Biomarkers and Classification and Regression Trees Can Discriminate Symptomatic from Asymptomatic Carotid Artery Disease Patients

2021 ◽  
Author(s):  
Aikaterini Trikouraki ◽  
Dido Yova ◽  
Abraham Pouliakis ◽  
Aris Spathis ◽  
Konstantinos G. Moulakakis ◽  
...  
Keyword(s):  
High Risk ◽  
Adhesion Molecule ◽  
Classification And Regression Tree ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Intercellular Adhesion Molecule ◽  
Matrix Metalloproteinase 1 ◽  
Asymptomatic Patients ◽  
Classification And Regression ◽  
Cart Model ◽  
Cart Algorithm

Abstract Objective To assess biomarkers between symptomatic and asymptomatic patients, and to construct a classification and regression tree (CART) algorithm for their discrimination. Patients and Methods 136 patients were enrolled. They were symptomatic (high risk) (N = 82, stenosis degree ≥ 50%, proven to be responsible for ischemic stroke the last six months) and asymptomatic (low risk) (N = 54, stenosis degree ≤ 50%). Levels of fibrinogen, matrix metalloproteinase-1 (MMP-1), tissue inhibitor of metalloproteinase-1 (TIMP-1), soluble intercellular adhesion molecule (SiCAM), soluble vascular cell adhesion molecule (SvCAM), adiponectin and insulin were measured on a Luminex 3D platform and their differences were evaluated; subsequently, a CART model was created and evaluated. Results All measured biomarkers, except adiponectin, had significantly higher levels in symptomatic patients. The constructed CART prognostic model had 97.6% discrimination accuracy on symptomatic patients and 79.6% on asymptomatic, while the overall accuracy was 90.4%. Moreover, the population was split into training and test sets for CART validation. Conclusion Significant differences were found in the biomarkers between symptomatic and asymptomatic patients. The CART model proved to be a simple decision-making algorithm linked with risk probabilities and provided evidence to identify and, therefore, treat patients being at high risk for cardiovascular disease.

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.

High risk of cutaneous melanoma amongst carriers of the intercellular adhesion molecule-1 R241 allele

2006 ◽  
Vol 16 (1) ◽  
pp. 93-96 ◽  
Author(s):  
Marco Vinceti ◽  
Giovanni Pellacani ◽  
Bruno Casali ◽  
Carlotta Malagoli ◽  
Davide Nicoli ◽  
...  
Keyword(s):  
High Risk ◽  
Adhesion Molecule ◽  
Cutaneous Melanoma ◽  
Intercellular Adhesion ◽  
Intercellular Adhesion Molecule ◽  
Intercellular Adhesion Molecule 1

scholarly journals A Prediction Model of Casualties Based on Machine Learning for Selection of Fire Scenario

2021 ◽  
Vol 21 (5) ◽  
pp. 165-173
Author(s):  
Donggoo Seo ◽  
Byunghun Park ◽  
Younghyun Lee ◽  
Wonhee Lee ◽  
Jungjae Kim ◽  
...  
Keyword(s):  
Regression Tree ◽  
Fire Risk ◽  
Classification And Regression Tree ◽  
Risk Assessments ◽  
Fire Prevention ◽  
Fire Growth ◽  
Fire Statistics ◽  
Classification And Regression ◽  
Cart Model ◽  
Selection Of

This study has developed a model that predicts casualties (dead and injured people) using the Classification And Regression Tree (CART). Based on the fire statistics collected over a decade, this model aims to select the appropriate risk-assessment scenarios and fire prevention and safety methods applicable on individual buildings. Our evaluation indicates that this CART model can accurately predict 48 scenarios based on 5 variables related to the types of fire, fire growth rates, and evacuation situations, and calculate the corresponding probabilities for each occurrence. This model is expected to improve future quantitative fire risk assessments.

scholarly journals Shallow landslide susceptibility mapping: A comparison between classification and regression tree and reduced error pruning tree algorithms

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Bahareh Ghasemain ◽  
Dawod Talebpoor Asl ◽  
Binh Thai Pham ◽  
Mohammadtghi Avand ◽  
Huu Duy Nguyen ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Regression Tree ◽  
Shallow Landslide ◽  
Susceptibility Mapping ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Landslide Susceptibility Mapping ◽  
Positive Rate ◽  
Classification And Regression ◽  
Cart Model

Shallow landslides through land degrading not only lead to threat the properly and life of human but they also may produce huge ecosystem damages. The aim of this study was to compare the performance of two decision tree machine learning algorithms including classification and regression tree (CART) and reduced error pruning tree (REPTree) for shallow landslide susceptibility mapping in Bijar, Kurdistan province, Iran. We first used 20 conditioning factors and then they were tested by information gain ratio (IGR) technique to select the most important ones. We then constructed a geodatabase based on the selected factors along with a total of 111 landslide locations with a ratio of 80/20 (for calibration/validation). The performance of the models was checked by the true positive rate (TP Rate), false positive rate (FP Rate), precision, recall, F1-Measure, Kappa, mean absolute error, and area under the receiver operatic curve (AUC). Results of IGR specified that the slope angle and TWI had the most contribution to shallow landslide occurrence in the study area. Moreover, results concluded that although these models had a high goodness-of-fit and prediction accuracy, the CART model (AUC=0.856) outperformed the REPTree model (AUC=0.837). Therefore, the CART model can be used as a promising tool and also as a base classifier to hybrid with optimization algorithms and Meta classifiers for spatial prediction of shallow landslide-prone areas.

scholarly journals PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS : DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019)

2020 ◽  
Vol 14 (2) ◽  
pp. 273-284
Author(s):  
Reni Pratiwi ◽  
Memi Nor Hayati ◽  
Surya Prangga
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Accuracy Rate ◽  
A Algorithm ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Classification And Regression ◽  
And Gender ◽  
Cart Algorithm

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). C5.0 algorithm is a non-binary decision tree where the branch of tree can be more than two, while the CART algorithm is a binary decision tree where the branch of tree consists of only two branches. This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method.   Keywords: C5.0 Algorithm, CART, Classification, Decision Tree.

scholarly journals Classification and Regression Tree (CART) model of sonographic signs in predicting thyroid nodules malignancy

2019 ◽  
Vol 6 ◽  
pp. 343-349
Author(s):  
Tarek Smayra ◽  
Zahra Charara ◽  
Ghassan Sleilaty ◽  
Gaelle Boustany ◽  
Lina Menassa-Moussa ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Classification And Regression ◽  
Cart Model

Heart Rate Variability measured during rest and after orthostatic challenge to detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification and Regression Tree model

2021 ◽  
pp. 1-18
Author(s):  
Shashikant Rathod ◽  
Leena Phadke ◽  
Uttam Chaskar ◽  
Chetankumar Patil
Keyword(s):  
Machine Learning ◽  
Autonomic Dysfunction ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Autonomic Response ◽  
Orthostatic Challenge ◽  
Percentage Difference ◽  
Classification And Regression ◽  
Cart Model

BACKGROUND: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing’s test and resting Heart Rate Variability (HRV) indices. OBJECTIVE: Resting HRV has limited use in screening due to its large intra and inter-individual variations. Therefore, a combined approach of resting and orthostatic challenge HRV measurement with a machine learning technique was used in the present study. METHODS: A total of 213 subjects of both genders between 20 to 70 years of age participated in this study from March 2018 to December 2019 at Smt. Kashibai Navale Medical College and General Hospital (SKNMCGH) in Pune, India. The volunteers were categorized according to their glycemic status as control (n= 51 Euglycemic) and T2DM (n= 162). The short-term ECG signal in the resting and after an orthostatic challenge was recorded. The HRV indices were extracted from the ECG signal as per HRV-Taskforce guidelines. RESULTS: We observed a significant difference in time, frequency, and non-linear resting HRV indices between the control and T2DM groups. A blunted autonomic response to an orthostatic challenge quantified by percentage difference was observed in T2DM compared to the control group. HRV patterns during rest and the orthostatic challenge were extracted by various machine learning algorithms. The classification and regression tree (CART) model has shown better performance among all the machine learning algorithms. It has shown an accuracy of 84.04%, the sensitivity of 89.51%, a specificity of 66.67%, with an Area Under Receiver Operating Characteristic Curve (AUC) of 0.78 compared to resting HRV alone with 75.12% accuracy, 86.42% sensitivity, 39.22% specificity, with an AUC of 0.63 for differentiating autonomic dysfunction in non-diabetic control and T2DM. CONCLUSION: It was possible to develop a Classification and Regression Tree (CART) model to detect autonomic dysfunction. The technique of percentage difference between resting and orthostatic challenge HRV indicates the blunted autonomic response. The developed CART model can differentiate the autonomic dysfunction using both resting and orthostatic challenge HRV data compared to only resting HRV data in T2DM. Thus, monitoring HRV parameters using the CART model during rest and after orthostatic challenge may be a better alternative to detect autonomic dysfunction in T2DM as against only resting HRV.

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