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.

scholarly journals Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia

2021 ◽  
Vol 20 (2) ◽  
pp. 417-426
Author(s):  
Pardomuan Robinson Sihombing ◽  
Istiqomatul Fajriyah Yuliati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Naive Bayes ◽  
Regression Tree ◽  
Imbalanced Data ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Classification And Regression ◽  
Sensitivity Specificity

Penelitian ini akan mengkaji penerapan beberapa metode machine learning dengan memperhatikan kasus imbalanced data dalam pemodelan klasifikasi untuk penentuan risiko kejadian bayi dengan BBLR yang diharapkan dapat menjadi solusi dalam menurunkan kelahiran bayi dengan BBLR di Indonesia. Adapun metode meachine learning yang digunakan adalah Classification and Regression Tree (CART), Naïve Bayes, Random Forest dan Support Vector Machine (SVM). Pemodelan klasifikasi dengan menggunakan teknik resample pada kasus imbalanced data dan set data besar terbukti mampu meningkatkan ketepatan klasifikasi khususnya terhadap kelas minoritas yang dapat diihat dari nilai sensitivity yang tinggi dibandingkan data asli (tanpa treatment). Selanjutnya, dari kelima model klasifikasi yang iuji menunjukkan bahwa model random forest memberikan kinerja terbaik berdasarkan nilai sensitivity, specificity, G-mean dan AUC tertinggi. Variabel terpenting/paling berpengaruh dalam klasifikasi resiko kejadian BBLR adalah jarak dan urutan kelahiran, pemeriksaan kehamilan, dan umur ibu

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.

scholarly journals The use of classification and regression tree when classifying winning and losing basketball teams

Kinesiology ◽  
2017 ◽  
Vol 49 (1) ◽  
pp. 47 ◽  
Author(s):  
Miguel A. Gómez ◽  
Sergio J. Ibáñez
Keyword(s):  
Parametric Analysis ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Total Variance ◽  
Related Variable ◽  
Situational Variables ◽  
Point Field ◽  
Classification And Regression ◽  
Non Parametric

<div>The aim of the present study was to identify the best predictors when classifying winning and losing teams in basketball in consideration of situational variables using the classification and regression tree (CRT) non-parametric analysis. The sample was composed of 1,404 balanced games (score-differences: 1-14 points) from the Spanish EBA Basketball League that presented high heterogeneity and a non-parametric distribution. These games were split into faster- and slower-paced games according to ball possessions per game (using a cluster k-means). The CRT analysis was used to predict which game-related variable/s better classified winning and losing teams during slower- and faster-paced games. In total, this approach explained 72% of the total variance in the slower- and 69.3% in the faster-paced games. The results identified importance of defensive-rebounds (100%), successful free-throws (94.7%), assists (86.1%), and fouls committed (55.9%) for the classification of winning and losing teams in the fast-paced games. Conversely, in the slow-paced games the better classification of winning or losing teams was accomplished by the following variables: successful free-throws (100%), defensive-rebounds (82.3%), fouls committed (68.4%), assists (66.9%), successful 2-point (62.2%) and 3-point field-goals (61.6%). The influence of situational variables was identified only for team quality in the slow-paced games. The present findings allow coaches for a better control of games and competition.</div>

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