Comparing the Performance of Naive Bayes And Decision Tree Classification Using R

Yadav, Kirtika; Thareja, Reema

doi:10.5815/ijisa.2019.12.02

Cited by 23 publications

(16 citation statements)

References 12 publications

(23 reference statements)

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“…For example, recent research by Maswadi et al, (2021) shows that NB and DT classification algorithms are used to classify human activities such as: sitting, standing, walking, sitting down, and standing up which improved accuracy and effectiveness. Yadav and Thareja, (2019) compare the performance of naive Bayes which use an assumption that the pair of features being classified are independent and DT classification uses a divide and conquer by calculating the accuracy of each technique using the R language.…”

Section: Related Workmentioning

confidence: 99%

“…This is due to their good performance and high accuracy in classifying obesity levels compared to SVM and KNN. DT is a high-performance classification algorithm that analysed data and uses a representation in a tree structure to gain a better insight into the data (Yadav & Thareja, 2019). NB is a classification technique based on the Bayes Theorem with an assumption of independence among predictors.…”

Section: Introductionmentioning

confidence: 99%

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Sule Lamido University Journal of Science and Technology (SLUJST) Vol. 3 No. 1&2 [June, 2022], pp. 113-121113Obesity Level ClassificationBased on Decision Tree and Naïve Bayes Classifiers

Garba¹,

Abdullahi²,

Umar³

et al. 2022

slujst

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This paper proposed an approach for obesity levels classification. The main contribution of this work is the use of boosting and bagging techniques in the decision tree (DT) and naïve Bayes (NB) classification model to improve the accuracy of obesity levels classification. This is achieved by introducing a boosting and bagging technique to further improve the recognition rate of obesity levels in the DT model, eliminating the correlated features, and eliminating the zero observations problem in the NB model. To validate the accuracy of the proposed approach, empirical evaluation was carried out using WEKA to determine the accuracy, precision, and recall. The results show that the DT classification model performs better in terms of accuracy and average precision. The proposed approach can help in software development for the classification of individuals with obesity

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sule Lamido University Journal of Science and Technology (SLUJST) Vol. 3 No. 1&2 [June, 2022], pp. 113-121113Obesity Level ClassificationBased on Decision Tree and Naïve Bayes Classifiers

Garba¹,

Abdullahi²,

Umar³

et al. 2022

slujst

View full text Add to dashboard Cite

show abstract

“…This technique is selected because several studies have shown that the Naïve-Bayesian method yields better overall evaluation result compared to other supervised learning methods. An experiment that compared the performance of Decision Tree and Naïve-Bayes algorithm in classifying student performance data indicated that Naïve-Bayes achieved higher accuracy and classi ed the values closer than Decision Tree [6].…”

Section: ) Mappingmentioning

confidence: 99%

An Automatic Data Mapping for Interoperability of OpenEMR Medical Practice Management Software Using the Fast Healthcare Interoperability Resources

Sujudi

Heryawan

2022

ABE

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Data compatibility in Electronic Medical Records (EMR) among healthcare facilities is necessary, especially for medical practitioners such as doctors or physicians, so that they can grant a more accurate decision on what treatments should be carried out for their patients, since a precise treatment or medication will increase the chance that patients would successfully heal from their disease. The compatibility of EMR data can also be called interoperability. This research attempts to apply interoperability of healthcare data by implementing an automatic mapper of an EMR data from one EMR management system called OpenEMR so that its data can meet the FHIR (Fast Healthcare Interoperability Resources) standard. Speci cally, a classi er to categorize the OpenEMR data into the appropriate FHIR format is discussed in this paper. There are three classi ers developed in Java and Python, which utilize the concepts of machine learning classi cation techniques; in this case, Naïve-Bayes and Decision Tree. Implementations of both machine learning algorithms showed a classication accuracy of 100%, which resulted in the additional implementation of rule-based technique, which also resulted in 100% accuracy. After running similar tests on all three implementations, the results infer that the rule-based technique is better than Naïve-Bayes for development in Java programming language.

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“…This paper [20] provides an overview of the decision tree and the Naive Bayes classification. The research utilized the student's output classification dataset to demonstrate how a decision tree and naive can be constructed and the principle of both classification strategies.…”

Section: A Application Of Naï Ve Bayes Algorithmmentioning

confidence: 99%

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

Perez¹,

Perez²

2021

IJMECS

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Data mining approaches provide different educational institutions opportunities to find hidden patterns from the data stored in the database. Many researchers have used these data to develop a model that would assist the institution administrators in decision-making. This study was performed to predict student program completion using the Naï ve Bayes classifier technique. The dataset utilized in this study was obtained from Bulacan State University -Sarmiento Campus in the Philippines under BS Information Technology program from five-year graduates' data for Academic Year 2012-2016. This dataset was pre-processed, cleansed, transformed, and balanced before constructing the model. Ten predictors were used for predicting student completion. The feature selection technique was used to filter and evaluate the significance of each factor. The significant variables assessed by the feature selection technique (Weight by Correlation) were the final parameters in creating the model. The Naï ve Bayes classifier was applied to predict the students' completion using the 70:30 ratios for training and testing dataset distribution. Correlation analysis identified the weight of individual attributes to the label attribute. From 10 possible predictor variables, only four (4) predictor variables were selected after correlation analysis. The identified significant attributes affecting program completion, namely (in order of significance): parents' monthly income, mother and father's educational attainment, and High School GPA attributes. The significant attributes identified in correlation analysis splitted into 70% training data or 447 records and 30% testing data or 191 records. There were 84 out of 191 data samples, or 44% of students were predicted to complete the program. On the other hand, 107 out of 191 data samples, or 56%, were predicted as not completing the program. The accuracy values performed an 84% rating with 80.46% class precision, and 83.33% class recall in the testing dataset (n=191). The outcomes of this study have a significant impact on HEIs, particularly on college completion rates. This study shall be highly significant and beneficial specifically to university administrators as this be a tool for them to identify students who will complete college based on variables included in the model.

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Comparing the Performance of Naive Bayes And Decision Tree Classification Using R

Cited by 23 publications

References 12 publications

Sule Lamido University Journal of Science and Technology (SLUJST) Vol. 3 No. 1&2 [June, 2022], pp. 113-121113Obesity Level ClassificationBased on Decision Tree and Naïve Bayes Classifiers

Sule Lamido University Journal of Science and Technology (SLUJST) Vol. 3 No. 1&2 [June, 2022], pp. 113-121113Obesity Level ClassificationBased on Decision Tree and Naïve Bayes Classifiers

An Automatic Data Mapping for Interoperability of OpenEMR Medical Practice Management Software Using the Fast Healthcare Interoperability Resources

Predicting Student Program Completion Using Naïve Bayes Classification Algorithm

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