scholarly journals Classification of Student Performance Dataset using Machine Learning Algorithms

2019 ◽  
Vol 9 (2S2) ◽  
pp. 752-757
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Student Performance ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

The scope of this research work is to identify the efficient machine learning algorithm for predicting the behavior of a student from the student performance dataset. We applied Support Vector Machines, K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms to predict the grade of a student and compared their prediction results in terms of various performance metrics. The students who visited many resources for reference, made academic related discussions and interactions in the class room, absent for minimum days, cared by parents care have shown great improvement in the final grade. Among the machine learning techniques we have used, SVM has shown more accuracy in terms of four important attribute. The accuracy rate of SVM after tuning is 0.80. The KNN and decision tree achieves the accuracy of 0.64, 0.65 respectively whereas the Naïve Bayes achieves 0.77.

scholarly journals Future Prediction of Diabetics using XG Booster Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 2128-2132
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Common Disease ◽  
Data Set ◽  
Glucose Content

Diabetes is a most common disease that occurs to most of the humans now a day. The predictions for this disease are proposed through machine learning techniques. Through this method the risk factors of this disease are identified and can be prevented from increasing. Early prediction in such disease can be controlled and save human’s life. For the early predictions of this disease we collect data set having 8 attributes diabetic of 200 patients. The patients’ sugar level in the body is tested by the features of patient’s glucose content in the body and according to the age. The main Machine learning algorithms are Support vector machine (SVM), naive bayes (NB), K nearest neighbor (KNN) and Decision Tree (DT). In the exiting the Naive Bayes the accuracy levels are 66% but in the Decision tree the accuracy levels are 70 to 71%. The accuracy levels of the patients are not proper in range. But in XG boost classifiers even after the Naïve Bayes 74 Percentage and in Decision tree the accuracy levels are 89 to 90%. In the proposed system the accuracy ranges are shown properly and this is only used mostly. A dataset of 729 patients can be stored in Mongo DB and in that 129 patients repots are taken for the prediction purpose and the remaining are used for training. The training datasets are used for the prediction purposes.

Machine Learning Based Coronary Artery Disease Prediction

2020 ◽  
Vol 17 (9) ◽  
pp. 3999-4002
Author(s):  
A. C. Bhavani ◽  
K. Aditya Shastry ◽  
K. Deepika ◽  
Nithya N. Shanbag ◽  
G. C. Akshatha
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Medical Diagnosis ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Health Organization

The world health organization (WHO) has assessed that the death of around 12 million people across the globe is observed each year because of diseases related to cardiovascular. The dangers associated with the cardiovascular disease can be identified effectively using machine learning techniques. As per survey, around 30% of the patient suffers no symptoms during heart attacks. But the bloodstream contains unique indications of the attack for days. The medical diagnosis of a patient remains a complex task due to several factors. The accurate medical diagnosis of a patient’s heart disease is critical as it significantly leads to the saving of millions of human lives. In this regard, the automation of the medical diagnosis is significant. The goal of this work is the development of a system for predicting the disease related to coronary artery in a patient with high accuracy utilizing machine learning (ML) techniques. Several algorithms like Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) classifiers were implemented for predicting the disease. Extensive experiments demonstrated that the naïve Bayes achieved higher accuracy than the DT and SVM with regards to accuracy, precision, F-Measure, Recall, and receiver operating characteristic (ROC) performance metrics.

A Comparative Analysis to Visualize the Behavior of Different Machine Learning Algorithms for Normalized and Un-Normalized Data in Predicting Alzheimer’s Disease

2019 ◽  
Vol 16 (9) ◽  
pp. 3840-3848
Author(s):  
Neeraj Kumar ◽  
Jatinder Manhas ◽  
Vinod Sharma
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Neurodegenerative Disorder ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Linear Discriminant ◽  
Age Related

Advancement in technology has helped people to live a long and better life. But the increased life expectancy has also elevated the risk of age related disorders, especially the neurodegenerative disorders. Alzheimer’s is one such neurodegenerative disorder, which is also the leading contributor towards dementia in elderly people. Despite of extensive research in this field, scientists have failed to find a cure for the disease till date. This makes early diagnosis of Alzheimer’s very crucial so as to delay its progression and improve the condition of the patient. Various techniques are being employed for diagnosing Alzheimer’s which include neuropsychological tests, medical imaging, blood based biomarkers, etc. Apart from this, various machine learning algorithms have been employed so far to diagnose Alzheimer’s in its early stages. In the current research, authors compared the performance of various machine learning techniques i.e., Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF) and Multi Layer Perceptron (MLP) on Alzheimer’s dataset. This paper experimentally demonstrated that normalization exhibits a predominant role in enhancing the efficiency of some machine learning algorithms. Therefore it becomes imperative to choose the algorithms as per the available data. In this paper, the efficiency of the given machine learning methods was compared in terms of accuracy and f1-score. Naïve Bayes gave a better overall performance for both accuracy and f1-score and it also remained unaffected with the normalization of data along with LDA, DT and RF. Whereas KNN, SVM and MLP showed a drastic (17% to 86%) improvement in the performance when they are given normalized data as compared to un-normalized data from Alzheimer’s dataset.

Analysis of Machine Learning Techniques for Anomaly-Based Intrusion Detection

2020 ◽  
Vol 12 (1) ◽  
pp. 20-38
Author(s):  
Winfred Yaokumah ◽  
Isaac Wiafe
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Naive Bayes ◽  
Weighted Average ◽  
Absolute Error ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques

Determining the machine learning (ML) technique that performs best on new datasets is an important factor in the design of effective anomaly-based intrusion detection systems. This study therefore evaluated four machine learning algorithms (naive Bayes, k-nearest neighbors, decision tree, and random forest) on UNSW-NB 15 dataset for intrusion detection. The experiment results showed that random forest and decision tree classifiers are effective for detecting intrusion. Random forest had the highest weighted average accuracy of 89.66% and a mean absolute error (MAE) value of 0.0252 whereas decision tree recorded 89.20% and 0.0242, respectively. Naive Bayes classifier had the worst results on the dataset with 56.43% accuracy and a MAE of 0.0867. However, contrary to existing knowledge, naïve Bayes was observed to be potent in classifying backdoor attacks. Observably, naïve Bayes performed relatively well in classes where tree-based classifiers demonstrated abysmal performance.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

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.

scholarly journals Cyber Bullying Detection for Twitter Using ML Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 24-29
Author(s):  
Muskan Patidar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Natural Language ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms

Abstract: Social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. Cyberbullying refers to the use of technology to humiliate and slander other people. It takes form of hate messages sent through social media and emails. With the exponential increase of social media users, cyberbullying has been emerged as a form of bullying through electronic messages. We have tried to propose a possible solution for the above problem, our project aims to detect cyberbullying in tweets using ML Classification algorithms like Naïve Bayes, KNN, Decision Tree, Random Forest, Support Vector etc. and also we will apply the NLTK (Natural language toolkit) which consist of bigram, trigram, n-gram and unigram on Naïve Bayes to check its accuracy. Finally, we will compare the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection. Keywords: Cyber bullying, Machine Learning Algorithms, Twitter, Natural Language Toolkit

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

2021 ◽  
Vol 6 (3) ◽  
pp. 35-47
Author(s):  
Anirudh Reddy Cingireddy ◽  
Robin Ghosh ◽  
Supratik Kar ◽  
Venkata Melapu ◽  
Sravanthi Joginipeli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Albert Einstein ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Blood Profile ◽  
Molecular Tests ◽  
Large Populations

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

scholarly journals Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

2020 ◽  
Vol 19 ◽  
pp. 153303382090982
Author(s):  
Melek Akcay ◽  
Durmus Etiz ◽  
Ozer Celik ◽  
Alaattin Ozen
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Nasopharyngeal Cancer ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tumor Diameter ◽  
Survival Prognosis ◽  
Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

scholarly journals Mobile Money Fraud Prediction—A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 383
Author(s):  
Francis Effirim Botchey ◽  
Zhen Qin ◽  
Kwesi Hughes-Lartey
Keyword(s):  
Developing Countries ◽  
Support Vector Machines ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobile Money ◽  
Vector Machines ◽  
Boosted Decision Tree

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.

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