Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree,k-nearest neighbors, and support vector machine

Abstract Background and Objective Multiple sclerosis (MS) is a chronic, progressive, and autoimmune disease of the central nervous system (CNS) characterized by inflammation, demyelination, and axonal injury. In patients with newly diagnosed MS (ndMS), ataxia can present either as mild or severe and can be difficult to diagnose in the absence of clinical disability. Such difficulties can be eliminated by using decision support systems supported by machine learning methods. The present study aimed to achieve early diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. Materials and Methods The prospective study included 32 ndMS patients with an Expanded Disability Status Scale (EDSS) score of≤2.0 and 32 healthy volunteers. A total of 14 parameters were elicited by using a Win-Track platform. The ndMS patients were differentiated from healthy individuals using multiple classifiers including Artificial Neural Network (ANN), Support Vector Machine (SVM), the k-nearest neighbors (K-NN) algorithm, and Decision Tree Learning (DTL). To improve the performance of the classification, a Relief-based feature selection algorithm was applied to select the subset that best represented the whole dataset. Performance evaluation was achieved based on several criteria such as Accuracy (ACC), Sensitivity (SN), Specificity (SP), and Precision (PREC). Results ANN had a higher classification performance compared to other classifiers, whereby it provided an accuracy, sensitivity, and specificity of 89, 87.8, 90.3% with the use of all parameters and provided the values of 93.7, 96.6%, and 91.1% with the use of parameters selected by the Relief algorithm, respectively. Significance To our knowledge, this is the first study of its kind in the literature to investigate the diagnosis of ataxia in ndMS patients by using machine learning methods with spatiotemporal parameters. The proposed method, i. e. Relief-based ANN method, successfully diagnosed ataxia by using a lower number of parameters compared to the numbers of parameters reported in clinical studies, thereby reducing the costs and increasing the performance of the diagnosis. The method also provided higher rates of accuracy, sensitivity, and specificity in the diagnosis of ataxia in ndMS patients compared to other methods. Taken together, these findings indicate that the proposed method could be helpful in the diagnosis of ataxia in minimally impaired ndMS patients and could be a pathfinder for future studies.

Download Full-text

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Atmosphere ◽

10.3390/atmos11010066 ◽

2020 ◽

Vol 11 (1) ◽

pp. 66 ◽

Cited By ~ 9

Author(s):

Sevda Shabani ◽

Saeed Samadianfard ◽

Mohammad Taghi Sattari ◽

Amir Mosavi ◽

Shahaboddin Shamshirband ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Gaussian Process ◽

Gaussian Process Regression ◽

Nearest Neighbors ◽

Support Vector ◽

Pan Evaporation ◽

Learning Methods ◽

K Nearest Neighbors ◽

Machine Learning Methods

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

Download Full-text

Avaliação de técnicas de inteligência computacional para identificação de atividades de vida diária

10.5753/sbcup.2018.3296 ◽

2018 ◽

Author(s):

Wylken S. Machado ◽

Pedro H. Barros ◽

Eliana S. Almeida ◽

Andre L. L. Aquino

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Sensor Networks ◽

Random Forest ◽

Decision Tree ◽

Nearest Neighbors ◽

Low Energy ◽

Support Vector ◽

Body Sensor Networks ◽

K Nearest Neighbors

Neste trabalho apresentamos a avaliação do desempenho de algoritmos de machine learning para identificar Atividades de Vida Diária (ADLs) e quedas. Nós avaliamos os seguintes algoritmos: K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Extra-Trees e Redes Neurais Recorrentes. Utilizamos um conjunto de dados coletados por uma Body Sensor Networks com cinco dispositivos sensores conectados através da interface Bluetooth Low Energy, chamado UMAFall. Obtivemos resultados satisfatórios, principalmente para as atividades saltar e queda frontal, com 100 % de acurácia, utilizando o algoritmo Extra-Trees.

Download Full-text

Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation

Journal of Personalized Medicine ◽

10.3390/jpm10030082 ◽

2020 ◽

Vol 10 (3) ◽

pp. 82

Author(s):

Man Hung ◽

Evelyn Lauren ◽

Eric Hon ◽

Julie Xu ◽

Bianca Ruiz-Negrón ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Support Vector Machine ◽

Catheter Ablation ◽

Decision Tree ◽

Hospital Readmissions ◽

Support Vector ◽

Day Hospital ◽

Learning Methods ◽

Machine Learning Methods

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient’s record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

Download Full-text

Development and research of algorithms for determining user preferred public transport stops in a geographic information system based on machine learning methods

Computer Optics ◽

10.18287/2412-6179-co-713 ◽

2020 ◽

Vol 44 (4) ◽

pp. 646-652

Author(s):

A.A. Borodinov

Keyword(s):

Machine Learning ◽

Public Transport ◽

Nearest Neighbors ◽

Support Vector ◽

Multi Layer Perceptron ◽

Calculation Algorithm ◽

Learning Methods ◽

K Nearest Neighbors ◽

Vector Method ◽

Machine Learning Methods

The paper considers a problem of determining the user preferred stops in a public transport recommender system. The effectiveness of using various machine learning methods to solve this problem in a system of personalized recommendations is compared, including a support vector method, a decision tree, a random forest, AdaBoost, a k-nearest neighbors algorithm, and a multi-layer perceptron. The described traditional methods of machine learning are also compared with the method proposed herein and based on an estimate calculation algorithm. The efficiency and the effectiveness of the proposed method are confirmed in the work.

Download Full-text

Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default

Review of Pacific Basin Financial Markets and Policies ◽

10.1142/s0219091519500218 ◽

2019 ◽

Vol 22 (03) ◽

pp. 1950021 ◽

Cited By ~ 1

Author(s):

Huei-Wen Teng ◽

Michael Lee

Keyword(s):

Machine Learning ◽

Risk Management ◽

Credit Risk ◽

Portfolio Management ◽

Credit Card ◽

Nearest Neighbors ◽

Support Vector ◽

Credit Risk Management ◽

Learning Methods ◽

Machine Learning Methods

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the [Formula: see text]-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

Download Full-text

Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network, support vector machine and fuzzy logic

Neural Computing and Applications ◽

10.1007/s00521-016-2694-9 ◽

2016 ◽

Vol 29 (12) ◽

pp. 1583-1591 ◽

Cited By ~ 4

Author(s):

Mohammad Abdolrazzaghi ◽

Soheil Hashemy ◽

Ali Abdolali

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Fuzzy Logic ◽

Inhomogeneous Media ◽

Support Vector ◽

Learning Methods ◽

Network Support ◽

Machine Learning Methods

Download Full-text

Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021824553 ◽

2021 ◽

Vol 8 (2) ◽

pp. 311

Author(s):

Mohammad Farid Naufal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Computer Vision ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Cross Validation ◽

Nearest Neighbors ◽

Support Vector ◽

Classification Algorithms ◽

K Nearest Neighbors

Cuaca merupakan faktor penting yang dipertimbangkan untuk berbagai pengambilan keputusan. Klasifikasi cuaca manual oleh manusia membutuhkan waktu yang lama dan inkonsistensi. Computer vision adalah cabang ilmu yang digunakan komputer untuk mengenali atau melakukan klasifikasi citra. Hal ini dapat membantu pengembangan self autonomous machine agar tidak bergantung pada koneksi internet dan dapat melakukan kalkulasi sendiri secara real time. Terdapat beberapa algoritma klasifikasi citra populer yaitu K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan Convolutional Neural Network (CNN). KNN dan SVM merupakan algoritma klasifikasi dari Machine Learning sedangkan CNN merupakan algoritma klasifikasi dari Deep Neural Network. Penelitian ini bertujuan untuk membandingkan performa dari tiga algoritma tersebut sehingga diketahui berapa gap performa diantara ketiganya. Arsitektur uji coba yang dilakukan adalah menggunakan 5 cross validation. Beberapa parameter digunakan untuk mengkonfigurasikan algoritma KNN, SVM, dan CNN. Dari hasil uji coba yang dilakukan CNN memiliki performa terbaik dengan akurasi 0.942, precision 0.943, recall 0.942, dan F1 Score 0.942. AbstractWeather is an important factor that is considered for various decision making. Manual weather classification by humans is time consuming and inconsistent. Computer vision is a branch of science that computers use to recognize or classify images. This can help develop self-autonomous machines so that they are not dependent on an internet connection and can perform their own calculations in real time. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). KNN and SVM are Machine Learning classification algorithms, while CNN is a Deep Neural Networks classification algorithm. This study aims to compare the performance of that three algorithms so that the performance gap between the three is known. The test architecture is using 5 cross validation. Several parameters are used to configure the KNN, SVM, and CNN algorithms. From the test results conducted by CNN, it has the best performance with 0.942 accuracy, 0.943 precision, 0.942 recall, and F1 Score 0.942.

Download Full-text

Prediction of Collapsibility of Loess of Construction Sites in Xining Based on Machine Learning Methods

10.21203/rs.3.rs-307514/v1 ◽

2021 ◽

Author(s):

Qifei Zhao ◽

Xiaojun Li ◽

Yunning Cao ◽

Zhikun Li ◽

Jixin Fan

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Training Data ◽

Support Vector ◽

Engineering Practice ◽

Burial Depth ◽

Learning Methods ◽

Data Set ◽

Machine Learning Methods ◽

North East

Abstract Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.

Download Full-text

A study and identification of COVID-19 viruses using N-grams with Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine

10.21203/rs.3.rs-40344/v2 ◽

2020 ◽

Author(s):

Mohamed El Boujnouni

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Decision Tree ◽

Naive Bayes ◽

Nearest Neighbors ◽

Support Vector ◽

K Nearest Neighbors ◽

The World ◽

Artificial Neural

Abstract Coronavirus disease 2019 or COVID-19 is a global health crisis caused by a virus officially named as severe acute respiratory syndrome coronavirus 2 and well known with the acronym (SARS-CoV-2). This very contagious illness has severely impacted people and business all over the world and scientists are trying so far to discover all useful information about it, including its potential origin(s) and inter-host(s). This study is a part of this scientific inquiry and it aims to identify precisely the origin(s) of a large set of genomes of SARS-COV-2 collected from different geographic locations in all over the world. This research is performed through the combination of five powerful techniques of machine learning (Naïve Bayes, K-Nearest Neighbors, Artificial Neural Networks, Decision tree and Support Vector Machine) and a widely known tool of language modeling (N-grams). The experimental results have shown that the majority of techniques gave the same global results concerning the origin(s) and inter-host(s) of SARS-COV-2. These results demonstrated that this virus has one zoonotic source which is Pangolin.

Download Full-text