Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

2019 ◽  
Vol 15 (3) ◽  
Author(s):  
Grzegorz M. Wójcik ◽  
Andrzej Kawiak ◽  
Lukasz Kwasniewicz ◽  
Piotr Schneider ◽  
Jolanta Masiak
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Event Related Potentials ◽  
Learning Tools ◽  
Mentally Disabled ◽  
Boosted Decision Tree ◽  
Brodmann Areas ◽  
Related Potentials ◽  
Somatosensory Responses

AbstractThe Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

scholarly journals Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

2021 ◽  
Vol 10 (1) ◽  
pp. 99
Author(s):  
Sajad Yousefi
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Disease ◽  
Decision Tree ◽  
Roc Curve ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Algorithm Performance ◽  
Machine Learning Models

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

scholarly journals Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study (Preprint)

2019 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Sen-Te Wang ◽  
Shiyng-Yu Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Significant Risk ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Examination ◽  
Multivariate Logistic Regression

BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

Machine Learning Techniques Applied to Profile Mobile Banking Users in India

2013 ◽  
Vol 5 (1) ◽  
pp. 82-92 ◽  
Author(s):  
M. Carr ◽  
V. Ravi ◽  
G. Sridharan Reddy ◽  
D. Veranna
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Decision Trees ◽  
Multilayer Perceptron ◽  
Machine Learning Techniques ◽  
Mobile Banking ◽  
Classification Rules ◽  
Learning Techniques ◽  
Potential Customers

This paper profiles mobile banking users using machine learning techniques viz. Decision Tree, Logistic Regression, Multilayer Perceptron, and SVM to test a research model with fourteen independent variables and a dependent variable (adoption). A survey was conducted and the results were analysed using these techniques. Using Decision Trees the profile of the mobile banking adopter’s profile was identified. Comparing different machine learning techniques it was found that Decision Trees outperformed the Logistic Regression and Multilayer Perceptron and SVM. Out of all the techniques, Decision Tree is recommended for profiling studies because apart from obtaining high accurate results, it also yields ‘if–then’ classification rules. The classification rules provided here can be used to target potential customers to adopt mobile banking by offering them appropriate incentives.

scholarly journals Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

2020 ◽  
Vol 10 (15) ◽  
pp. 5047 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Danesh Zandi ◽  
Himan Shahabi ◽  
Kamran Chapi ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Shallow Landslide ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Algorithm ◽  
Alternating Decision Tree ◽  
Bayesian Logistic Regression

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

scholarly journals Comparison Decision Tree and Logistic Regression Machine Learning Classification Algorithms to determine Covid-19

SinkrOn ◽  
2022 ◽  
Vol 7 (1) ◽  
pp. 59-65
Author(s):  
Artika Arista
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Cross Validation ◽  
Performance Testing ◽  
Signs And Symptoms ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Wide Range ◽  
Testing Performance

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.

scholarly journals Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study

10.2196/17110 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e17110 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Sen-Te Wang ◽  
Shiyng-Yu Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Significant Risk ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Examination ◽  
Multivariate Logistic Regression

Background Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. Objective We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. Methods Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. Results Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. Conclusions Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

scholarly journals The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson’s Disease

2021 ◽  
Author(s):  
Robin Vlieger ◽  
Elena Daskalaki ◽  
Deborah Apthorp ◽  
Christian J Lueck ◽  
Hanna Suominen
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Objective Assessment ◽  
Diagnostic Testing ◽  
Event Related Potentials ◽  
Disease Status ◽  
Related Potentials ◽  
Machine Learning Models

Current tests of disease status in Parkinson’s disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson’s disease.

scholarly journals Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261947
Author(s):  
Sharon Hassin-Baer ◽  
Oren S. Cohen ◽  
Simon Israeli-Korn ◽  
Gilad Yahalom ◽  
Sandra Benizri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Event Related Potentials ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Network Analytics ◽  
Related Potentials ◽  
Advanced Analysis

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

scholarly journals Mirror Mechanism Behind Visual–Auditory Interaction: Evidence From Event-Related Potentials in Children With Cochlear Implants

2021 ◽  
Vol 15 ◽  
Author(s):  
Junbo Wang ◽  
Jiahao Liu ◽  
Kaiyin Lai ◽  
Qi Zhang ◽  
Yiqing Zheng ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Mirror Neurons ◽  
Recognition Task ◽  
Event Related Potentials ◽  
Brodmann Area ◽  
Event Related Potential ◽  
Visual Speech ◽  
Brodmann Areas ◽  
Related Potentials

The mechanism underlying visual-induced auditory interaction is still under discussion. Here, we provide evidence that the mirror mechanism underlies visual–auditory interactions. In this study, visual stimuli were divided into two major groups—mirror stimuli that were able to activate mirror neurons and non-mirror stimuli that were not able to activate mirror neurons. The two groups were further divided into six subgroups as follows: visual speech-related mirror stimuli, visual speech-irrelevant mirror stimuli, and non-mirror stimuli with four different luminance levels. Participants were 25 children with cochlear implants (CIs) who underwent an event-related potential (ERP) and speech recognition task. The main results were as follows: (1) there were significant differences in P1, N1, and P2 ERPs between mirror stimuli and non-mirror stimuli; (2) these ERP differences between mirror and non-mirror stimuli were partly driven by Brodmann areas 41 and 42 in the superior temporal gyrus; (3) ERP component differences between visual speech-related mirror and non-mirror stimuli were partly driven by Brodmann area 39 (visual speech area), which was not observed when comparing the visual speech-irrelevant stimulus and non-mirror groups; and (4) ERPs evoked by visual speech-related mirror stimuli had more components correlated with speech recognition than ERPs evoked by non-mirror stimuli, while ERPs evoked by speech-irrelevant mirror stimuli were not significantly different to those induced by the non-mirror stimuli. These results indicate the following: (1) mirror and non-mirror stimuli differ in their associated neural activation; (2) the visual–auditory interaction possibly led to ERP differences, as Brodmann areas 41 and 42 constitute the primary auditory cortex; (3) mirror neurons could be responsible for the ERP differences, considering that Brodmann area 39 is associated with processing information about speech-related mirror stimuli; and (4) ERPs evoked by visual speech-related mirror stimuli could better reflect speech recognition ability. These results support the hypothesis that a mirror mechanism underlies visual–auditory interactions.

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