scholarly journals Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart

Jurnal Varian ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 37-46
Author(s):  
Gede Suwardika ◽  
I Ketut Putu Suniantara ◽  
Ni Putu Nanik Hendayanti
Keyword(s):  
Predictive Accuracy ◽  
Classification Tree ◽  
Regression Tree ◽  
Majority Voting ◽  
Classification And Regression Tree ◽  
Combination Method ◽  
Student Graduation ◽  
Classification Prediction ◽  
The Stability

The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.

scholarly journals ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA

2019 ◽  
Vol 13 (3) ◽  
pp. 177-184
Author(s):  
Gede Suwardika Suwardika ◽  
I Ketut Putu Suniantara
Keyword(s):  
Random Forest ◽  
Classification Tree ◽  
Regression Tree ◽  
University Student ◽  
Classification And Regression Tree ◽  
Random Forest Method ◽  
Student Graduation ◽  
Stability And Accuracy ◽  
Learning Data

Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.

scholarly journals Validation and verification of predictive salivary biomarkers for oral health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nagihan Bostanci ◽  
Konstantinos Mitsakakis ◽  
Beral Afacan ◽  
Kai Bao ◽  
Benita Johannsen ◽  
...  
Keyword(s):  
Oral Health ◽  
Predictive Accuracy ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Regression Tree ◽  
High Sensitivity ◽  
Oral Health Status ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Salivary Biomarkers

AbstractOral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

scholarly journals A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Tamara Ius ◽  
Fabrizio Pignotti ◽  
Giuseppe Maria Della Pepa ◽  
Giuseppe La Rocca ◽  
Teresa Somma ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Classification Tree ◽  
Prognostic Score ◽  
Regression Tree ◽  
Image Data ◽  
Extent Of Resection ◽  
Classification And Regression Tree ◽  
Postoperative Treatment ◽  
Time Data ◽  
Cart Analysis

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

scholarly journals Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

Foods ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 274 ◽  
Author(s):  
Mohammed Gagaoua ◽  
Valérie Monteils ◽  
Sébastien Couvreur ◽  
Brigitte Picard
Keyword(s):  
Decision Tree ◽  
Supervised Learning ◽  
Predictive Accuracy ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Beef Tenderness ◽  
Interaction Detection ◽  
Fiber Area ◽  
Total Collagen ◽  
Protected Designation Of Origin

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the instrumental Warner-Bratzler Shear force (WBSF). Accordingly, 60 variables including WBSF and belonging to 4 levels of the continuum that are farm-slaughterhouse-muscle-meat were analyzed by Partial Least Squares (PLS) and three decision tree methods (C&RT: classification and regression tree; QUEST: quick, unbiased, efficient regression tree and CHAID: Chi-squared Automatic Interaction Detection) to select the driving factors of beef tenderness and propose predictive decision tools. The former method retained 24 variables from 59 to explain 75% of WBSF. Among the 24 variables, six were from farm level, four from slaughterhouse level, 11 were from muscle level which are mostly protein biomarkers, and three were from meat level. The decision trees applied on the variables retained by the PLS model, allowed identifying three WBSF classes (Tender (WBSF ≤ 40 N/cm2), Medium (40 N/cm2 < WBSF < 45 N/cm2), and Tough (WBSF ≥ 45 N/cm2)) using CHAID as the best decision tree method. The resultant model yielded an overall predictive accuracy of 69.4% by five splitting variables (total collagen, µ-calpain, fiber area, age of weaning and ultimate pH). Therefore, two decision model rules allow achieving tender meat on PDO Maine-Anjou cows: (i) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain ≥ 169 arbitrary units (AU)) AND (ultimate pH < 5.55) THEN meat was very tender (mean WBSF values = 36.2 N/cm2, n = 12); or (ii) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain < 169 AU) AND (age of weaning < 7.75 months) AND (fiber area < 3100 µm2) THEN meat was tender (mean WBSF values = 39.4 N/cm2, n = 30).

scholarly journals Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

2019 ◽  
Vol 16 (11) ◽  
pp. 1935 ◽  
Author(s):  
Ying Yao ◽  
Xiaohua Zhao ◽  
Hongji Du ◽  
Yunlong Zhang ◽  
Guohui Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Drunk Driving ◽  
Driving Performance ◽  
Classification And Regression Tree ◽  
Vehicle Speed ◽  
Road Geometry ◽  
Lane Position ◽  
Straight Segments

It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.

scholarly journals Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 10-16
Author(s):  
Rachmadania Irmanita ◽  
Sri Suryani Prasetiyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Medical Treatment ◽  
Severe Malaria ◽  
Naive Bayes ◽  
Regression Tree ◽  
Naïve Bayes ◽  
Classification And Regression Tree ◽  
Second Step ◽  
Anopheles Mosquitoes ◽  
Classification And Regression

Malaria is a disease caused by the Plasmodium parasite that transmitted by female Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical treatment. The late medical treatment happened because of misdiagnosis and lack of medical staff, especially in the countryside. This problem can cause severe malaria that has complications. This study creates a system prediction to classify the severe malaria disease using Classification and Regression Tree (CART) method and the probability of malaria complication using Naïve Bayes method. The first step of this study is classifying the patients that have symptom are infected severe malaria or not based on the model that has been built. The next step, if the patient classified severe malaria then the data predicted if there any probability of complication by the malaria. There are 8 possibilities of complication malaria which are convulsion, hypoglycemia, hyperpyrexia, and the combinations of these four. The first step will evaluate by using F-score, precision and recall while the second step will evaluate by using accuracy. The highest result F-score, precision and recall are 0.551, 0.471 and 0.717. The highest accuracy 81.2% which predicted the complication is Hypoglycemia.

Classification of 3G Mobile Phone Customers

2009 ◽  
pp. 2862-2870
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
The Third ◽  
Adaptive Regression ◽  
Classification And Regression ◽  
3G Mobile Phone ◽  
Adaptive Regression Splines

In this article we discuss how we have predicted the third generation (3G) customers using logistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population

Cutaneous complications in hematopoietic cell transplant recipients: Impact of biopsy on patient management.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 7036-7036
Author(s):  
Oana Valeria Paun ◽  
Tycel Jovelle Phillips ◽  
PingFu Fu ◽  
Robert Novoa ◽  
Kord Honda ◽  
...  
Keyword(s):  
Classification Tree ◽  
Regression Tree ◽  
Hematopoietic Cell ◽  
Classification And Regression Tree ◽  
Cell Transplant ◽  
Hematopoietic Cell Transplant ◽  
Data Set ◽  
Post Transplant ◽  
Skin Biopsies ◽  
The Impact

7036 Background: Although skin biopsies are recommended for diagnostic purposes in hematopoietic cell transplant (HCT) recipients, their utility in directing management of post transplant cutaneous eruptions remains uncertain. Little evidence was found in support of this procedure either from a diagnostic or prognostic perspective. Methods: We retrospectively evaluated 351 consecutive HCT recipients transplanted at our institution between January 2005 and December 2011; 156 patients underwent 388 cutaneous biopsies. Results: The group that underwent cutaneous biopsy after transplantation and the group that was spared the procedure were homogenous with regards to age and gender. The pre-biopsy diagnosis and final diagnosis differed in 213 episodes (55%) as determined by histologic evaluation. Biopsy results led to a change in therapy in 61 of 388 (16%) biopsied rashes. With regards to therapy changes, 24 of 61 (39%) occurred in response to a clinical diagnosis of GVHD. In this situation the most frequently noted change was augmentation or addition of systemic immuno-suppression (19 of 24). Changes in systemic therapy occurred with similar frequencies with respect to concordance or discordance between clinical and histopathologic diagnosis (p = 0.12). We used classification and regression tree analysis to develop an algorithm to predict the biopsy yield as expressed by change of management. This is a non-parametric decision tree learning technique that produces a classification tree based on a categorical dependent variable, formed by a collection of rules based on variables in the modeling data set. Conclusions: Cutaneous biopsy findings often changed the clinical dermatologic diagnoses of HCT recipients; however, the impact of biopsy results on treatment decisions was less profound; altered diagnoses in patients who underwent biopsy often did not lead to therapy changes. Skin biopsies of post-transplant patients may not be mandatory for either diagnostic or therapeutic reasons, but in carefully chosen circumstances can yield extremely important data. A prospective study should be undertaken in order to evaluate current practice data and to validate our decision making analysis tree.

scholarly journals Over-indebted Households in Poland: Classification Tree Analysis

2020 ◽  
Author(s):  
Grzegorz Wałęga ◽  
Agnieszka Wałęga
Keyword(s):  
Classification Tree ◽  
Regression Tree ◽  
Poverty Line ◽  
Classification And Regression Tree ◽  
Vulnerable Groups ◽  
Psychological Burden ◽  
Tree Analysis ◽  
Level Data ◽  
Income Ratio ◽  
Definition Of

Abstract Increasing a personal debt burden implies greater financial vulnerability and threats for macroeconomic stability. It also generates a risk of the households over-indebtedness. The assessment of over-indebtedness is conducted with the use of various objective and subjective measures based on the micro-level data. The aim of the study is to investigate over-indebted households in Poland using a unique dataset obtained from the CATI survey. We discuss and compare the usefulness of various over-indebtedness measures across different socio-economic characteristics. Due to the differences in over-indebtedness across single measures, we perform a more complex assessment using a mix of indicators. As an alternative to other commonly criticised over-indebtedness measures, we apply the “below the poverty line” (BPL) measure. In order to obtain the profile of over-indebted households, we use classification and regression tree analysis as an alternative to logit or probit models. We find that DSTI (“debt service to income”) ratio underestimates the extent of over-indebtedness in vulnerable groups of households in comparison with the BPL. We highlight the necessity to use different measures depending on the adopted definition of over-indebtedness. A psychological burden of debts is particularly strong among older and poorly educated respondents. We also find that the age structure of over-indebted households in Poland differs from this structure in countries with a broader access to consumer credits. Our results can be used to enrich the methods of assessing the household over-indebtedness.

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