scholarly journals Analysis of Chi-square Automatic Interaction Detection (CHAID) and Classification and Regression Tree (CRT) for Classification of Corn Production

2017 ◽  
Vol 909 ◽  
pp. 012041 ◽  
Author(s):  
Yuliana Susanti ◽  
Etik Zukhronah ◽  
Hasih Pratiwi ◽  
Respatiwulan ◽  
H. Sri Sulistijowati
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Chi Square ◽  
Corn Production ◽  
Interaction Detection ◽  
Classification And Regression

scholarly journals Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

2021 ◽  
Author(s):  
Peng Song ◽  
Shengwei Ren ◽  
Yu Liu ◽  
Pei Li ◽  
Qingyan Zeng
Keyword(s):  
Decision Tree ◽  
Amplitude Ratio ◽  
Regression Tree ◽  
Biomechanical Model ◽  
Study Data ◽  
Classification And Regression Tree ◽  
Stiffness Parameter ◽  
Chi Square ◽  
Interaction Detection ◽  
Cart Model

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

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

Classification Of 3G Mobile Phone Customers

2011 ◽  
pp. 2247-2254
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
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 lo-gistic 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.

Classification Of 3G Mobile Phone Customers

2008 ◽  
pp. 2558-2565
Author(s):  
Ankur Jain ◽  
Lalit Wangikar ◽  
Martin Ahrens ◽  
Ranjan Rao ◽  
Suddha Sattwa Kundu ◽  
...  
Keyword(s):  
Regression Tree ◽  
Multivariate Adaptive Regression Splines ◽  
Classification And Regression Tree ◽  
Third Generation ◽  
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 lo-gistic 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.

scholarly journals 2121 Quantitative structural knee measurements improve classification of accelerated knee osteoarthritis: Data from the osteoarthritis initiative

2018 ◽  
Vol 2 (S1) ◽  
pp. 25-25
Author(s):  
Lori L. Price ◽  
Timothy E. McAlindon ◽  
Mamta Amin ◽  
Charles B. Eaton ◽  
Julie E. Davis ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Cruciate Ligament ◽  
Regression Tree ◽  
Serum Glucose ◽  
Classification And Regression Tree ◽  
Tree Analysis ◽  
Regression Tree Analysis ◽  
Osteoarthritis Initiative ◽  
Classification And Regression

OBJECTIVES/SPECIFIC AIMS: The aim of this study is to determine whether quantitative measures of knee structures including effusion, bone marrow lesions, cartilage, and meniscal damage can improve upon an existing model of demographic and clinical characteristics to classify accelerated knee osteoarthritis (AKOA). METHODS/STUDY POPULATION: We conducted a case-control study using data from baseline and four annual follow-up visits from the osteoarthritis initiative. Participants had no radiographic knee osteoarthritis (KOA) at baseline. AKOA is defined as progressing from no KOA to advance-stage KOA in at least 1 knee within 48 months. AKOA knees were matched 1:1 based on sex to (1) participants who did not develop KOA within 48 months and (2) participants who developed KOA but not AKOA. Analyses were person based. Classification and regression tree analysis was used to determine the important variables and percent of variance explained. RESULTS/ANTICIPATED RESULTS: A previous classification and regression tree analysis found that age, BMI, serum glucose, and femorotibial angle explained 31% of the variability between those who did and did not develop AKOA. Including structural measurements as candidate variables yielded a model that included effusion, BMI, serum glucose, cruciate ligament degeneration and coronal slope and explained 39% of the variability. DISCUSSION/SIGNIFICANCE OF IMPACT: Knee structural measurements improve classification of participants who developed AKOA Versus those who did not. Further research is needed to better classify patients at risk for AKOA.

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