Analysis of Nave Bayes Classification for Diabetes Mellitus

Sankaranarayanan, Sriram; Perumal, T. Pramananda

doi:10.26438/ijcse/v6i12.520524

Cited by 3 publications

(4 citation statements)

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“…The Naive Bayes classifier can also be used for continuous and categorical variables. It is based on the Bayes formula which is the probability of event A given proof of B which can be seen in the following equation [27]:…”

Section: Classification With Naïve Bayesmentioning

confidence: 99%

Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications

Religia

Pranoto

Suwancita

2021

JISA

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The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%.

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Section: Classification With Naïve Bayesmentioning

confidence: 99%

Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications

Religia

Pranoto

Suwancita

2021

JISA

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“…The related work is discussed in Copyright © 2019 MECS I.J. Intelligent Systems and Applications, 2019, 12, [11][12][13][14][15][16][17][18][19] Section II. In section III, we have given brief description about decision tree and what is used to build a decision tree.…”

Section:  Classification Algorithmmentioning

confidence: 99%

“…Naive Bayes classifier can be used for both continuous and categorical variables. It is based on the Bayes formula that is the probability of event A given B evidence which is given above also as [11]:…”

Section: B Naive Bayes Classificationmentioning

confidence: 99%

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

Yadav¹,

Thareja²

2019

IJISA

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The use of technology is at its peak. Many companies try to reduce the work and get an efficient result in a specific amount of time. But a large amount of data is being processed each day that is being stored and turned into large datasets. To get useful information, the dataset needs to be analyzed so that one can extract knowledge by training the machine. Thus, it is important to analyze and extract knowledge from a large dataset. In this paper, we have used two popular classification techniques-Decision tree and Naive Bayes to compare the performance of the classification of our data set. We have taken student performance dataset that has 480 observations. We have classified these students into different groups and then calculated the accuracy of our classification by using the R language. Decision tree uses a divide and conquer method including some rules that makes it easy for humans to understand. The Naive Bayes theorem includes an assumption that the pair of features being classified are independent. It is based on the Bayes theorem.

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“…In a previous study, produce a decision support system application with an accuracy level of 81.18% [15]. Therefore, this method was chosen because it was considered simple but produced accurate results [16].…”

mentioning

confidence: 99%

Measure the effectiveness of information systems with the naïve bayes classifier method

Triayudi

Sumiati

Dwiyatno

et al. 2021

IJ-AI

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<span id="docs-internal-guid-a31a7709-7fff-65df-d360-f52175b11d47"><span>Technological advances at this time are developing very fast, information systems became the frontline in technological advancements, the need for information systems to support jobs is increasingly high. However, its implementation for users does not have a significant impact, so that it needs to be reviewed and re-evaluated in the use of the information system built. The naive bayes classifier method can provide "effective" and "ineffective" conclusions and is used as material for evaluation and improvement. The purpose of this study is to contribute to measuring the effectiveness of the information system, to solve problems with the naïve bayes classifier method approach which has advantages in the process of classifying data and predicting data. From the test results three times, training has been conducted using 100 data, accuracy value of 84.82% and error 15.18%.</span></span>

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Analysis of Nave Bayes Classification for Diabetes Mellitus

Cited by 3 publications

References 0 publications

Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications

Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications

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

Measure the effectiveness of information systems with the naïve bayes classifier method

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