scholarly journals Diabetic Prediction using Classification Method

2020 ◽  
Vol 9 (2) ◽  
pp. 264-267
Keyword(s):  
Diabetes Mellitus ◽  
Feature Extraction ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Classification Models ◽  
Performance Parameters ◽  
Prediction Analysis ◽  
Input Dataset

Prediction analysis of diabetes mellitus is the main focus of this work. There are mainly three tasks involved in prediction analysis. These tasks are input dataset, feature extraction and classification. The earlier framework makes use of SVM and naïve bayes approaches for predicting this disease. This study implements voting classifier for prediction purpose. It is an ensemble approach. This classifier combines three classification models. These models are SVM, naïve bayes and decision tree. The implementation of available and new technique is carried out in python tool. These approaches give outcomes in terms of different performance parameters. In contrast to other classification models, proposed classification model performs better.

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

Energies ◽  
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.

scholarly journals Text Analysis of Applicants for Personality Classification Using Multinomial Naïve Bayes and Decision Tree

2020 ◽  
Vol 12 (3) ◽  
Author(s):  
Nanda Yonda Hutama ◽  
Kemas Muslim Lhaksmana ◽  
Isman Kurniawan
Keyword(s):  
Decision Tree ◽  
Personality Traits ◽  
Big Five ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Big Five Personality ◽  
Text Data ◽  
Personality Classification ◽  
Best Parameters

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.

scholarly journals A Machine Learning Approach for Improving the Movement of Humanoid NAO’s Gaits

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fatmah Abdulrahman Baothman
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Average Velocity ◽  
Walking Speed ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Models ◽  
Ann Model ◽  
The Real ◽  
Optimal Average

A humanoid robot’s development requires an incredible combination of interdisciplinary work from engineering to mathematics, software, and machine learning. NAO is a humanoid bipedal robot designed to participate in football competitions against humans by 2050, and speed is crucial for football sports. Therefore, the focus of the paper is on improving NAO speed. This paper is aimed at testing the hypothesis of whether the humanoid NAO walking speed can be improved without changing its physical configuration. The applied research method compares three classification techniques: artificial neural network (ANN), Naïve Bayes, and decision tree to measure and predict NAO’s best walking speed, then select the best method, and enhance it to find the optimal average velocity speed. According to Aldebaran documentation, the real NAO’s robot default walking speed is 9.52 cm/s. The proposed work was initiated by studying NAO hardware platform limitations and selecting Nao’s gait 12 parameters to measure the accuracy metrics implemented in the three classification models design. Five experiments were designed to model and trace the changes for the 12 parameters. The preliminary NAO’s walking datasets open-source available at GitHub, the NAL, and RoboCup datasheets are implemented. All generated gaits’ parameters for both legs and feet in the experiments were recorded using the Choregraphe software. This dataset was divided into 30% for training and 70% for testing each model. The recorded gaits’ parameters were then fed to the three classification models to measure and predict NAO’s walking best speed. After 500 training cycles for the Naïve Bayes, the decision tree, and ANN, the RapidMiner scored 48.20%, 49.87%, and 55.12%, walking metric speed rate, respectively. Next, the emphasis was on enhancing the ANN model to reach the optimal average velocity walking speed for the real NAO. With 12 attributes, the maximum accuracy metric rate of 65.31% was reached with only four hidden layers in 500 training cycles with a 0.5 learning rate for the best walking learning process, and the ANN model predicted the optimal average velocity speed of 51.08% without stiffness: V 1 = 22.62   cm / s , V 2 = 40   cm / s , and V = 30   cm / s . Thus, the tested hypothesis holds with the ANN model scoring the highest accuracy rate for predicting NAO’s robot walking state speed by taking both legs to gauge joint 12 parameter values.

scholarly journals Evaluating the Performance of Supervised Classification Models: Decision Tree and Naïve Bayes Using KNIME

2018 ◽  
Vol 7 (4.5) ◽  
pp. 248 ◽  
Author(s):  
Syed Muzamil Basha ◽  
Dharmendra Singh Rajput ◽  
Ravi Kumar Poluru ◽  
S. Bharath Bhushan ◽  
Shaik Abdul Khalandar Basha
Keyword(s):  
Decision Tree ◽  
Classification Accuracy ◽  
Supervised Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Task ◽  
Classification Models ◽  
Target Variable ◽  
Input Variables ◽  
F Measure

The classification task is to predict the value of the target variable from the values of the input variables. If a target is provided as part of the dataset, then classification is a supervised task. It is important to analysis the performance of supervised classification models before using them in classification task. In our research we would like to propose a novel way to evaluated the performance of supervised     classification models like Decision Tree and Naïve Bayes using KNIME Analytics platform. Experiments are conducted on Multi variant dataset consisting 58000 instances, 9 columns associated specially for classification, collected from UCI Machine learning repositories  (http://archive.ics.uci.edu/ml/datasets/statlog+(shuttle)) and compared the performance of both the models in terms of Classification  Accuracy (CA) and Error Rate. Finally, validated both the models using Metric precision, recall and F-measure. In our finding, we found that  Decision tree acquires CA (99.465%) where as Naïve Bayes attain CA (90.358%). The F-measure of Decision tree is 0.984, whereas Naïve Bayes acquire 0.7045.  

scholarly journals PEMBENTUKAN MODEL KLASIFIKASI DATA LAMA STUDI MAHASISWA STMIK INDONESIA MENGGUNAKAN DECISION TREE DENGAN ALGORITMA NBTREE

Kilat ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 145-148
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Assessment Criteria ◽  
Study Program

One of the assessment criteria for the accreditation of the study program is the assessment of the duration of the study of students who graduated on time. not a few students who pursue the study period exceeds the established standard of graduation. So it is important for the study program to know which students have the possibility of passing is not timely. For that it is necessary to predict the length of student study. One way to predict the length of a student's study is to build a classification model. This study aims to build a long prediction model of student study using Decision Tree with NBTree algorithm. The data used are academic value data and student academic leave data. The result obtained is a classification model of Naïve Bayes Decision Tree with 73.45% accuracy.

scholarly journals Automation of gender determination in human canines using artificial intelligence

2018 ◽  
Vol 50 (3) ◽  
pp. 116
Author(s):  
F. Fidya ◽  
Bayu Priyambadha
Keyword(s):  
Artificial Intelligence ◽  
Sexual Dimorphism ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Accuracy Rate ◽  
Identification Process ◽  
Gender Determination ◽  
Lower Canine

Background: Gender determination is an important aspect of the identification process. The tooth represents a part of the human body that indicates the nature of sexual dimorphism. Artificial intelligence enables computers to perform to the same standard the same tasks as those carried out by humans. Several methods of classification exist within an artificial intelligence approach to identifying sexual dimorphism in canines. Purpose: This study aimed to quantify the respective accuracy of the Naive Bayes, decision tree, and multi-layer perceptron (MLP) methods in identifying sexual dimorphism in canines. Methods: A sample of results derived from 100 measurements of the diameter of mesiodistal, buccolingual, and diagonal upper and lower canine jaw models of both genders were entered into an application computer program that implements the algorithm (MLP). The analytical process was conducted by the program to obtain a classification model with testing being subsequently carried out in order to obtain 50 new measurement results, 25 each for males and females. A comparative analysis was conducted on the program-generated information. Results: The accuracy rate of the Naive Bayes method was 82%, while that of the decision tree and MLP amounted to 84%. The MLP method had an absolute error value lower than that of its decision tree counterpart. Conclusion: The use of artificial intelligence methods produced a highly accurate identification process relating to the gender determination of canine teeth. The most appropriate method was the MLP with an accuracy rate of 84%.

scholarly journals The KNIME based classification models for yellow fever virus inhibition

RSC Advances ◽  
2015 ◽  
Vol 5 (19) ◽  
pp. 14663-14669 ◽  
Author(s):  
N. S. Hari Narayana Moorthy ◽  
Vasanthanathan Poongavanam
Keyword(s):  
Yellow Fever ◽  
Naive Bayes ◽  
Yellow Fever Virus ◽  
Naïve Bayes ◽  
Fever Virus ◽  
Classification Model ◽  
Classification Models ◽  
Bayes Method ◽  
Virus Inhibition

The Naïve Bayes method as implemented in KNIME platform for classification of YFV inhibition. The best classification model is able to correctly discriminate >90% of inhibitors and non-inhibitors.

scholarly journals Data Mining Application in Predicting Bank Loan Defaulters

2020 ◽  
Vol 9 (4) ◽  
pp. 2733-2744
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Model Building ◽  
Naive Bayes ◽  
Large Data ◽  
Naïve Bayes ◽  
Bank Loan ◽  
Classification Model ◽  
Data Set ◽  
Data Mining Application

Data mining is the key tools for discoveries of knowledge from large data set. Nowadays, most of the organizations using this technology to maintain their data. This paper focuses on the Bank sector in Risk management specifically, detecting Bank loan defaulters through the data mining application to examine the patterns of different attribute which would contribute for detecting and predicting defaulters thus preventing wrong loans. This process can be done without change the current systems and the data. Then it helps to distinguish borrowers who repay loans promptly from those who don’t and avoid wrong loan allotment. In order to show the results of the study Classification model is implemented in order to find interesting patterns among attributes of customer. A total of 20461 sample data were taken by data base admin randomly from 3 consecutive years from the Bank database to build and test the model. In this research we used Classification model of decision tree and Naïve Bayes in Weka 3.7 tool for experiments. Modeling methodology applied to this paper was CIRSP-DM (Cross Industry Standard for Data Mining), which involves business understanding, data understanding, data preparation, model building, evaluation and deployment. Decision tree classifications with J48 implementation with 8 experiments were performed. Two experiments with different parameters were made for Naïve Bayes. Finally, evaluation and analysis of the models were performed then given a best solution to predict the defaulters.

scholarly journals Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement

2019 ◽  
Vol 2 (2) ◽  
pp. 58 ◽  
Author(s):  
Utomo Pujianto ◽  
Asa Luki Setiawan ◽  
Harits Ar Rosyid ◽  
Ali M. Mohammad Salah
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Naive Bayes ◽  
Feature Selection Method ◽  
Selection Method ◽  
Naïve Bayes ◽  
The Body ◽  
Classification Model ◽  
Diabetic Patients ◽  
Patient Readmissions

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.

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