scholarly journals A Machine Learning-Based Intelligent System for Predicting Diabetes

2019 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Nabila Shahnaz Khan ◽  
Mehedi Hasan Muaz ◽  
Anusha Kabir ◽  
Muhammad Nazrul Islam
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Personal Health ◽  
Diabetic Patients ◽  
Technological Growth ◽  
Bayes Algorithm ◽  
The Comparative Study

In this era of technological growth, the diagnosis of diseases and finding cures, personal health parameter management and predicting the possibility of susceptibility to some diseases have become accessible and easy. Although all over the world millions of people are falling victim to diabetes, in most of the cases they are not even aware of their situation due to the silent nature of diabetes. Therefore, the objective of this research is to propose an intelligent system based on a machine learning algorithm to improve the accuracy of predicting diabetes. To attain this objective, an algorithm was proposed based on Naïve Bayes with prior clustering. Second, the performance of the proposed algorithm was evaluated using 532 data related to diabetic patients. Finally, the performance of the existing Naïve Bayes algorithm was compared with the proposed algorithm. The results of the comparative study showed that the improvement in the accuracy has been made apparent for the proposed algorithm.

scholarly journals A Machine Learning Framework for Improving Classification Performance on Credit Approval

2021 ◽  
Vol 10 (1) ◽  
pp. 47-52
Author(s):  
Pulung Hendro Prastyo ◽  
Septian Eko Prasetyo ◽  
Shindy Arti
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Information Gain ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Credit Scoring ◽  
Research Work ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Bayes Algorithm

Credit scoring is a model commonly used in the decision-making process to refuse or accept loan requests. The credit score model depends on the type of loan or credit and is complemented by various credit factors. At present, there is no accurate model for determining which creditors are eligible for loans. Therefore, an accurate and automatic model is needed to make it easier for banks to determine appropriate creditors. To address the problem, we propose a new approach using the combination of a machine learning algorithm (Naïve Bayes), Information Gain (IG), and discretization in classifying creditors. This research work employed an experimental method using the Weka application. Australian Credit Approval data was used as a dataset, which contains 690 instances of data. In this study, Information Gain is employed as a feature selection to select relevant features so that the Naïve Bayes algorithm can work optimally. The confusion matrix is used as an evaluator and 10-fold cross-validation as a validator. Based on experimental results, our proposed method could improve the classification performance, which reached the highest performance in average accuracy, precision, recall, and f-measure with the value of 86.29%, 86.33%, 86.29%, 86.30%, and 91.52%, respectively. Besides, the proposed method also obtains 91.52% of the ROC area. It indicates that our proposed method can be classified as an excellent classification.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

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 Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

2019 ◽  
Vol 9 (2S) ◽  
pp. 53-56
Keyword(s):  
Naive Bayes ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Confusion Matrix ◽  
Daily Basis ◽  
Naïve Bayes ◽  
Human Beings ◽  
Waste Generation ◽  
Future Prediction ◽  
Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

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%).

scholarly journals Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm

Informatica ◽  
2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Ernest Kwame Ampomah ◽  
Gabriel Nyame ◽  
Zhiguang Qin ◽  
Prince Clement Addo ◽  
Enoch Opanin Gyamfi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Stock Market Prediction

scholarly journals Accuracy Enhancement of Diabetic Retinopathy Detection Using Naive Bayes Algorithm

2021 ◽  
Vol 9 (VII) ◽  
pp. 3648-3652
Author(s):  
Mithileshkumar Yadav
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Automated System ◽  
Diabetic Patients ◽  
Detection Accuracy ◽  
Monitoring Model ◽  
Bayes Algorithm ◽  
Retinal Fundus

Diabetic retinopathy (DR) is a disease of eye which is caused by diabetes. Sometime the DR leads the diabetic patients to complete vision loss. In this scenario, early identification of DR is more essential to protect the eyesight and provide help for timely treatment. The detection of DR can be done manually by ophthalmologists and can also be done by an automated system. An ophthalmologist is required to analyze and explain retinal fundus images in the manual system, which is a time consuming and very expensive task. While, In the automated system, artificial intelligence is used to perform an significant role in the area of ophthalmology and specifically in the early detection of DR over the traditional detection approaches. Recently, numerous advanced studies related to the identification of DR have been reported, But still research for accurate detection of DR is going on. In this paper, a new diabetic retinopathy monitoring model is proposed by using the Naive Bayes method to improve the accuracy of detection of DR. The model is trained on mixture of two datasets Messidor and Kaggle, and evaluated on the Messidor dataset. By using proposed method detection accuracy is found to be higher than existing methods.

Disease Prediction using Naïve Bayes - Machine Learning Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 17-22
Author(s):  
Chandrasekhar Rao Jetti ◽  
Rehamatulla Shaik ◽  
Sadhik Shaik
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Healthcare System ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques ◽  
Smart Healthcare

It can occur on many occasions that you or a loved one requires urgent medical assistance, but they are unavailable due to unforeseen circumstances, or that we are unable to locate the appropriate doctor for the care. As a result, we will try to incorporate an online intelligent Smart Healthcare System in this project to solve this issue. It's a web-based programmed that allows patients to get immediate advice about their health problems. The aim of the smart healthcare system is to create a web application that can take a user's symptoms and predict diseases, as well as serve as an online consultant for various diseases. We created an expert system called Smart Health Care System, which is used to make doctors' jobs easier. A machine examines a patient at a basic level and recommends diseases that may be present. It begins by inquiring about the patient's symptoms; if the device is able to determine the relevant condition, it then recommends a doctor in the patient's immediate vicinity. The system will show the result based on the available accumulated data. We're going to use some clever data mining techniques here. We use several intelligent data mining techniques to guess the most accurate illness that could be associated with a patient's symptoms, and we use an algorithm (Naive Bayes) to map the symptoms with potential diseases based on a database of many patients' medical records. This system not only makes doctors' jobs easier, but it also benefits patients by getting them the care they need as soon as possible. Keywords: Disease Prediction, Naïve Bayes, Machine Learning Algorithm, Smart Healthcare System.

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