Classification of Personality Type by Typology Hippocrates - Galenus Using Hybrid Naïve Bayes Decision Tree Algorithm

Aim: The main aim of the study proposed is to perform higher classification of fake political news by implementing fake news detectors using machine learning classifiers by comparing their performance. Materials and Methods: By considering two groups such as Decision Tree algorithm and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which consists of 44,000 records. Through the programming experiment which is performed using N=10 iterations on each algorithm to identify various scales of fake news and true news classification. Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in. There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance when compared to Naive Bayes algorithm.

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Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.7.10634 ◽

2018 ◽

Vol 7 (1.7) ◽

pp. 137 ◽

Cited By ~ 1

Author(s):

Danda Shashank Reddy ◽

Chinta Naga Harshitha ◽

Carmel Mary Belinda

Keyword(s):

Brain Tumor ◽

Decision Tree ◽

Naive Bayes ◽

Classification Problem ◽

Naïve Bayes ◽

World Health ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Computer Tomography Scan ◽

Bayes Algorithm

Now a day’s many advanced techniques are proposed in diagnosing the tumor in brain like magnetic resonance imaging, computer tomography scan, angiogram, spinal tap and biospy. Based on diagnosis it is easy to predict treatment. All of the types of brain tumor are officially reclassified by the World Health Organization. Brain tumors are of 120 types, almost each tumor is having same symptoms and it is difficult to predict treatment. For this regard we are proposing more accurate and efficient algorithm in predicting the type of brain tumor is Naïve Bayes’ classification and decision tree algorithm. The main focus is on solving tumor classification problem using these algorithms. Here the main goal is to show that the prediction through the decision tree algorithm is simple and easy than the Naïve Bayes’ algorithm.

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Analisis Perbandingan Algoritma Decision Tree, kNN, dan Naive Bayes untuk Prediksi Kesuksesan Start-up

JISKA (Jurnal Informatika Sunan Kalijaga) ◽

10.14421/jiska.2021.6.3.178-188 ◽

2021 ◽

Vol 6 (3) ◽

pp. 178-188

Author(s):

Adhitya Prayoga Permana ◽

Kurniyatul Ainiyah ◽

Khadijah Fahmi Hayati Holle

Keyword(s):

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Test Results ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Start Up ◽

The Many ◽

Start Ups

Start-ups have a very important role in economic growth, the existence of a start-up can open up many new jobs. However, not all start-ups that are developing can become successful start-ups. This is because start-ups have a high failure rate, data shows that 75% of start-ups fail in their development. Therefore, it is important to classify the successful and failed start-ups, so that later it can be used to see the factors that most influence start-up success, and can also predict the success of a start-up. Among the many classifications in data mining, the Decision Tree, kNN, and Naïve Bayes algorithms are the algorithms that the authors chose to classify the 923 start-up data records that were previously obtained. The test results using cross-validation and T-test show that the Decision Tree Algorithm is the most appropriate algorithm for classifying in this case study. This is evidenced by the accuracy value obtained from the Decision Tree algorithm, which is greater than other algorithms, which is 79.29%, while the kNN algorithm has an accuracy value of 66.69%, and Naive Bayes is 64.21%.

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ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

SinkrOn ◽

10.33395/sinkron.v4i1.10150 ◽

2019 ◽

Vol 4 (1) ◽

pp. 88 ◽

Cited By ~ 1

Author(s):

Sumpena Sumpena ◽

Yuma Akbar ◽

Nirat Nirat ◽

Mario Hengky

Keyword(s):

Intensive Care ◽

Decision Tree ◽

Medical Information ◽

Naive Bayes ◽

Venous Pressure ◽

Naïve Bayes ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Sepsis Diagnosis ◽

Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.

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To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1976.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 2429-2433

Keyword(s):

Academic Performance ◽

Decision Tree ◽

Naive Bayes ◽

Research Work ◽

Naïve Bayes ◽

Classification Algorithms ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Science College ◽

Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

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COMPARATIVE STUDY OF DECISION TREE ALGORITHM AND NAIVE BAYES CLASSIFIER FOR SWINE FLU PREDICTION

International Journal of Research in Engineering and Technology ◽

10.15623/ijret.2015.0406007 ◽

2015 ◽

Vol 04 (06) ◽

pp. 45-50 ◽

Cited By ~ 1

Author(s):

Mangesh J. Shinde .

Keyword(s):

Decision Tree ◽

Comparative Study ◽

Naive Bayes ◽

Naïve Bayes ◽

Swine Flu ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Decision Tree Algorithm ◽

Naïve Bayes Classifier ◽

Tree Algorithm

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Classification of diabetes disease using decision tree algorithm (C4.5)

Journal of Physics Conference Series ◽

10.1088/1742-6596/1869/1/012082 ◽

2021 ◽

Vol 1869 (1) ◽

pp. 012082

Author(s):

B A C Permana ◽

R Ahmad ◽

H Bahtiar ◽

A Sudianto ◽

I Gunawan

Keyword(s):

Decision Tree ◽

Decision Tree Algorithm ◽

Tree Algorithm

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Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽

10.3390/en13082067 ◽

2020 ◽

Vol 13 (8) ◽

pp. 2067

Author(s):

Nilsa Duarte da Silva Lima ◽

Irenilza de Alencar Nääs ◽

João Gilberto Mendes dos Reis ◽

Raquel Baracat Tosi Rodrigues da Silva

Keyword(s):

Decision Tree ◽

High Efficiency ◽

Rating Scale ◽

Naive Bayes ◽

Naïve Bayes ◽

Environmental Efficiency ◽

Classification Model ◽

Bayes Algorithm ◽

J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

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