Comparison of Machine Learning Classification Methods in Hepatitis C Virus

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.

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Predicting Student’s Performance Using Machine Learning Algorithm

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1209 ◽

2021 ◽

pp. 53-58

Author(s):

Sheela Rani P ◽

Dhivya S ◽

Dharshini Priya M ◽

Dharmila Chowdary A

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Prediction Model ◽

Naive Bayes ◽

Learning Algorithm ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

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Effectiveness of Classification Methods on the Diabetes System

Asian Journal of Research in Computer Science ◽

10.9734/ajrcos/2021/v12i330287 ◽

2021 ◽

pp. 33-43

Author(s):

Ahmed T. Shawky ◽

Ismail M. Hagag

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Early Stage ◽

Research Paper ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbor ◽

Machine Learning Classification ◽

Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

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Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes

The African Journal of Information and Communication ◽

10.23962/10539/29195 ◽

2020 ◽

pp. 1-13

Author(s):

Fillemon S. Enkono ◽

Nalina Suresh

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Mobile Applications ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Svm Classifier ◽

Machine Learning Classification

Fraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.

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Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7287.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 557-561

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Naïve Bayes ◽

Gradient Boosting ◽

Svm Classifier ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Machine Learning Classification ◽

Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

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On the Analysis of Machine Learning Classifiers to Detect Traffic Congestion in Vehicular Networks

10.5753/eniac.2019.9290 ◽

2019 ◽

Author(s):

Lucas Carvalho ◽

Maycon Silva ◽

Edimilson Santos ◽

Daniel Guidoni

Keyword(s):

Machine Learning ◽

Traffic Congestion ◽

Vehicular Networks ◽

Naive Bayes ◽

Naïve Bayes ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbors ◽

Applied Machine Learning ◽

Routing Methods

Problems related to traffic congestion and management have become common in many cities. Thus, vehicle re-routing methods have been proposed to minimize the congestion. Some of these methods have applied machine learning techniques, more specifically classifiers, to verify road conditions and detect congestion. However, better results may be obtained by applying a classifier more suitable to domain. In this sense, this paper presents an evaluation of different classifiers applied to the identification of the level of road congestion. Our main goal is to analyze the characteristics of each classifier in this task. The classifiers involved in the experiments here are: Multiple Layer Neural Network (MLP), K-Nearest Neighbors (KNN), Decision Trees (J48), Support Vector Machines (SVM), Naive Bayes and Tree Augment Naive Bayes.

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Classification of Aggressive Movements Using Smartwatches

Sensors ◽

10.3390/s20216377 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6377

Author(s):

Franck Tchuente ◽

Natalie Baddour ◽

Edward D. Lemaire

Keyword(s):

Machine Learning ◽

Random Forest ◽

Aggressive Behavior ◽

Naive Bayes ◽

Poor Performance ◽

Naïve Bayes ◽

Classification Model ◽

Support Vector ◽

Care Providers ◽

K Nearest Neighbors

Recognizing aggressive movements is a challenging task in human activity recognition. Wearable smartwatch technology with machine learning may be a viable approach for human aggressive behavior classification. This research identified a viable classification model and feature selector (CM-FS) combination for separating aggressive from non-aggressive movements using smartwatch data and determined if only one smartwatch is sufficient for this task. A ranking method was used to select relevant CM-FS models across accuracy, sensitivity, specificity, precision, F-score, and Matthews correlation coefficient (MCC). The Waikato environment for knowledge analysis (WEKA) was used to run 6 machine learning classifiers (random forest, k-nearest neighbors (kNN), multilayer perceptron neural network (MP), support vector machine, naïve Bayes, decision tree) coupled with three feature selectors (ReliefF, InfoGain, Correlation). Microsoft Band 2 accelerometer and gyroscope data were collected during an activity circuit that included aggressive (punching, shoving, slapping, shaking) and non-aggressive (clapping hands, waving, handshaking, opening/closing a door, typing on a keyboard) tasks. A combination of kNN and ReliefF was the best CM-FS model for separating aggressive actions from non-aggressive actions, with 99.6% accuracy, 98.4% sensitivity, 99.8% specificity, 98.9% precision, 0.987 F-score, and 0.984 MCC. kNN and random forest classifiers, combined with any of the feature selectors, generated the top models. Models with naïve Bayes or support vector machines had poor performance for sensitivity, F-score, and MCC. Wearing the smartwatch on the dominant wrist produced the best single-watch results. The kNN and ReliefF combination demonstrated that this smartwatch-based approach is a viable solution for identifying aggressive behavior. This wrist-based wearable sensor approach could be used by care providers in settings where people suffer from dementia or mental health disorders, where random aggressive behaviors often occur.

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Using Machine Learning to Build a Classification Model for IoT Networks to Detect Attack Signatures

International journal of Computer Networks & Communications ◽

10.5121/ijcnc.2020.12607 ◽

2020 ◽

Vol 12 (6) ◽

pp. 99-116

Author(s):

Mousa Al-Akhras ◽

Mohammed Alawairdhi ◽

Ali Alkoudari ◽

Samer Atawneh

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Denial Of Service ◽

Learning Algorithms ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Classification Model ◽

Security And Privacy ◽

K Nearest Neighbors ◽

Detection Model

Internet of things (IoT) has led to several security threats and challenges within society. Regardless of the benefits that it has brought with it to the society, IoT could compromise the security and privacy of individuals and companies at various levels. Denial of Service (DoS) and Distributed DoS (DDoS) attacks, among others, are the most common attack types that face the IoT networks. To counter such attacks, companies should implement an efficient classification/detection model, which is not an easy task. This paper proposes a classification model to examine the effectiveness of several machine-learning algorithms, namely, Random Forest (RF), k-Nearest Neighbors (KNN), and Naïve Bayes. The machine learning algorithms are used to detect attacks on the UNSW-NB15 benchmark dataset. The UNSW-NB15 contains normal network traffic and malicious traffic instants. The experimental results reveal that RF and KNN classifiers give the best performance with an accuracy of 100% (without noise injection) and 99% (with 10% noise filtering), while the Naïve Bayes classifier gives the worst performance with an accuracy of 95.35% and 82.77 without noise and with 10% noise, respectively. Other evaluation matrices, such as precision and recall, also show the effectiveness of RF and KNN classifiers over Naïve Bayes.

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Detecting Kidney Disease using Naïve Bayes and Decision Tree in Machine Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4377.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 498-501

Keyword(s):

Machine Learning ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Decision Tree ◽

Naive Bayes ◽

Kidney Diseases ◽

Naïve Bayes ◽

Bayes Classifier ◽

Machine Learning Classification ◽

The Relationship

Chronic Kidney Disease (CKD) mostly influence patients suffered from difficulties due to diabetes or high blood pressure and make them unable to carry out their daily activities. In a survey , it has been revealed that one in 12 persons living in two biggest cities of India diagnosed of CKD features that put them at high risk for unfavourable outcomes. In this article, we have analyzed as well as anticipated chronic kidney disease by discovering the hidden pattern of the relationship using feature selection and Machine Learning classification approach like naive Bayes classifier and decision tree(J48). The dataset on which these approaches are applied is taken from UC Irvine repository. Based on certain feature, the approaches will predict whether a person is diagnosed with a CKD or Not CKD. While performing comparative analysis, it has been observed that J48 decision tree gives high accuracy rate in prediction. J48 classifier proves to be efficient and more effective in detecting kidney diseases.

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Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.201854773 ◽

2018 ◽

Vol 5 (4) ◽

pp. 427 ◽

Cited By ~ 1

Author(s):

Riri Nada Devita ◽

Heru Wahyu Herwanto ◽

Aji Prasetya Wibawa

Keyword(s):

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Classification Methods ◽

K Nearest Neighbor ◽

Bayes Method ◽

K Nearest Neighbors ◽

Naive Bayes Method ◽

Main Factor

Kecocokan isi artikel dengan sebuah tema jurnal menjadi faktor utama diterima tidaknya sebuah artikel. Tetapi masih banyak mahasiswa yang bingung untuk menentukan jurnal yang sesuai dengan artikel yang dimilikinya. Untuk itu diperlukannya sebuah metode klasifikasi dokumen yang dapat mengelompokkan artikel secara otomatis dan akurat. Terdapat banyak metode klasifikasi yang dapat digunakan. Metode yang digunakan dalam penelitian ini adalah Naive Bayes dan sebagai baseline digunakan metode K-Nearest Neighbor. Metode Naive Bayes dipilih karena dapat menghasilkan akurasi yang maksimal dengan data latih yang sedikit. Sedangkan metode K-Nearest Neighbor dipilih karena metode tersebut tangguh terhadap data noise. Kinerja dari kedua metode tersebut akan dibandingkan, sehingga dapat diketahui metode mana yang lebih baik dalam melakukan klasifikasi dokumen. Hasil yang didapatkan menunjukkan metode Naive Bayes memiliki kinerja yang lebih baik dengan tingkat akurasi 70%, sedangkan metode K-Nearest Neighbor memiliki tingkat akurasi yang cukup rendah yaitu 40%. AbstractOne way to be accepted in a journal conference and get the publication is to create an article with perfect suitability content of the journal. Matching the content of the article with a journal theme is the main factor for acceptability an article. But there are still many students who are confused to choose the journal in accordance with the articles it has. So we need a method to classification article documents category automatically and accurately group articles. There are many classification methods that can be used. The method used in this study is Naive Bayes and as a baseline the K-Nearest Neighbor method. Naive Bayes method is chosen because it can produce maximum accuracy with little training data. While K-Nearest Neighbor method was chosen because the method is robust to data noise. The performance of the two methods will be compared, so we can be known which method is better in classifying the document. The results show that the Naive Bayes method performs is more accurate with 70% accuracy and K-Nearest Neighbors method has a fairly low accuracy of 40% on classification test.

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