scholarly journals Using Machine Learning Algorithm to Describe the Connection between the Types and Characteristics of Music Signal

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bo Sun
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Machine Learning Algorithm ◽  
Music Classification ◽  
Music Signal

Music classification is conducive to online music retrieval, but the current music classification model finds it difficult to accurately identify various types of music, which makes the classification effect of the current music classification model poor. In order to improve the accuracy of music classification, a music classification model based on multifeature fusion and machine learning algorithm is proposed. First, we obtain the music signal, and then extract various features from the classification of the music signal, and use machine learning algorithms to describe the type of music signal and the relationship between the features. The music classifier and deep belief network machine learning models in shallow logistic regression are established, respectively. Experiments were designed for these two models to verify the applicability of the model for music classification. By comparing the experimental results, it is found that the classification accuracy of the deep confidence network model is higher than that of the logistic regression model, but the number of iterations needed for its accuracy to converge is also higher than that of the logistic regression model. Compared with other current music classification models, this model reduces the time of constructing music classifier, speeds up the speed of music classification, and can identify various types of music with high precision. The accuracy of music classification is obviously improved, which verifies the superiority of this music classification model.

scholarly journals Predicting the risk of inpatient hypoglycemia with machine learning using electronic health records

2020 ◽  
Author(s):  
Yue Ruan ◽  
Alexis Bellot ◽  
Zuzana Moysova ◽  
Garry D. Tan ◽  
Alistair Lumb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Oxygen Saturation Level ◽  
Electronic Health ◽  
Clinically Significant

<b><i>Objective </i></b> <p>We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.<i></i></p> <p><b><i>Research Design and Methods </i></b></p> <p>Four years of data was extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycaemic episodes (BG <u><</u> 3.9 and <u><</u> 2.9mmol/L respectively). We used patient demographics, administered medications, vital signs, laboratory results and procedures performed during the hospital stays to inform the model. Two iterations of the dataset included the doses of insulin administered and the past history of inpatient hypoglycaemia. Eighteen different prediction models were compared using the area under curve of the receiver operating characteristics (AUC_ROC) through a ten-fold cross validation.</p> <p><b><i>Results</i></b> </p> <p>We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, metformin) and albumin levels. The machine learning model with the best performance was the XGBoost model (AUC_ROC 0.96. This outperformed the logistic regression model which had an AUC_ROC of 0.75 for the estimation of the risk of clinically significant hypoglycaemia.<b><i></i></b></p> <p><b><i>Conclusions</i></b></p> <p>Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycaemia.</p>

scholarly journals Predicting the risk of inpatient hypoglycemia with machine learning using electronic health records

2020 ◽  
Author(s):  
Yue Ruan ◽  
Alexis Bellot ◽  
Zuzana Moysova ◽  
Garry D. Tan ◽  
Alistair Lumb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Oxygen Saturation Level ◽  
Electronic Health ◽  
Clinically Significant

<b><i>Objective </i></b> <p>We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms.<i></i></p> <p><b><i>Research Design and Methods </i></b></p> <p>Four years of data was extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycaemic episodes (BG <u><</u> 3.9 and <u><</u> 2.9mmol/L respectively). We used patient demographics, administered medications, vital signs, laboratory results and procedures performed during the hospital stays to inform the model. Two iterations of the dataset included the doses of insulin administered and the past history of inpatient hypoglycaemia. Eighteen different prediction models were compared using the area under curve of the receiver operating characteristics (AUC_ROC) through a ten-fold cross validation.</p> <p><b><i>Results</i></b> </p> <p>We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, metformin) and albumin levels. The machine learning model with the best performance was the XGBoost model (AUC_ROC 0.96. This outperformed the logistic regression model which had an AUC_ROC of 0.75 for the estimation of the risk of clinically significant hypoglycaemia.<b><i></i></b></p> <p><b><i>Conclusions</i></b></p> <p>Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycaemia.</p>

A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson’s Disease Dementia Based on Clinical and Neuropsychological Scores

2021 ◽  
pp. 089198872199355
Author(s):  
Anastasia Bougea ◽  
Efthymia Efthymiopoulou ◽  
Ioanna Spanou ◽  
Panagiotis Zikos
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Neuropsychological Tests ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Classification Model ◽  
Support Vector ◽  
Ensemble Model ◽  
Machine Learning Algorithm

Objective: Our aim was to develop a machine learning algorithm based only on non-invasively clinic collectable predictors, for the accurate diagnosis of these disorders. Methods: This is an ongoing prospective cohort study ( ClinicalTrials.gov identifier NCT number NCT04448340) of 78 PDD and 62 DLB subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. We used predictors such as clinico-demographic characteristics, 6 neuropsychological tests (mini mental, PD Cognitive Rating Scale, Brief Visuospatial Memory test, Symbol digit written, Wechsler adult intelligence scale, trail making A and B). We investigated logistic regression, K-Nearest Neighbors (K-NNs) Support Vector Machine (SVM), Naïve Bayes classifier, and Ensemble Model for their ability to predict successfully PDD or DLB diagnosis. Results: The K-NN classification model had an accuracy 91.2% of overall cases based on 15 best clinical and cognitive scores achieving 96.42% sensitivity and 81% specificity on discriminating between DLB and PDD. The binomial logistic regression classification model achieved an accuracy of 87.5% based on 15 best features, showing 93.93% sensitivity and 87% specificity. The SVM classification model had an accuracy 84.6% of overall cases based on 15 best features achieving 90.62% sensitivity and 78.58% specificity. A model created on Naïve Bayes classification had 82.05% accuracy, 93.10% sensitivity and 74.41% specificity. Finally, an Ensemble model, synthesized by the individual ones, achieved 89.74% accuracy, 93.75% sensitivity and 85.73% specificity. Conclusion: Machine learning method predicted with high accuracy, sensitivity and specificity PDD or DLB diagnosis based on non-invasively and easily in-the-clinic and neuropsychological tests.

scholarly journals THETA: Machine-learning Algorithm for Evaluating Hybrid Operation Theater Entry of Trauma Patients

2020 ◽  
Author(s):  
Atsushi Senda ◽  
Akira Endo ◽  
Takahiro Kinoshita ◽  
Yasuhiro Otomo
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Web Application ◽  
Logistic Regression Model ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Operating Rooms ◽  
Classification And Regression Tree ◽  
Trauma Patients

Abstract Background The clinical benefits of hybrid operating rooms are recognized globally. However, appropriate conditions for entry into such rooms must be urgently established, because they exclusively benefit few patients under severe trauma while requiring a significant amount of resources. This paper presents an algorithm to triage trauma patients into a hybrid operating room. Methods This retrospective observational study was conducted using the Japan Trauma Data Bank database comprising information collected between January 2004 and December 2018. A machine-learning-based triage algorithm is developed using the baseline demographics, injury mechanisms, and vital signs obtained from the database. The analysis dataset comprised information regarding 117,771 trauma patients with abbreviated injury scale (AIS) > 3. The performance of the proposed model was compared against those of other statistical models (logistic regression and classification and regression tree [CART] models) while considering the status quo entry condition (systolic blood pressure < 90 mmHg). Results The proposed trauma hybrid-suite entry algorithm (THETA) outperforms other algorithms (PR-AUC: THETA [0.59], logistic regression model [0.22], and CART [0.20]; AUROC: THETA [0.93], logistic regression model [0.88], and CART [0.86]), thereby facilitating appropriate triaging of patients who would potentially benefit from resuscitation performed using angiographic percutaneous techniques and operative resuscitation suites. Conclusions An accurate machine-learning-based algorithm is developed to triage patient entry into hybrid operating rooms via a web application, thereby enabling emergency doctors to utilize limited medical resources more efficiently.

A survey on prediction of diabetes using classification algorithms

2021 ◽  
Vol 2 (104) ◽  
pp. 77-84
Author(s):  
A. Khanwalkar ◽  
R. Soni
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Learning Algorithm ◽  
Algorithm Design ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Collection Method ◽  
Data Collection Method ◽  
Diagnostic Center

Purpose: Diabetes is a chronic disease that pays for a large proportion of the nation's healthcare expenses when people with diabetes want medical care continuously. Several complications will occur if the polymer disorder is not treated and unrecognizable. The prescribed condition leads to a diagnostic center and a doctor's intention. One of the real-world subjects essential is to find the first phase of the polytechnic. In this work, basically a survey that has been analyzed in several parameters within the poly-infected disorder diagnosis. It resembles the classification algorithms of data collection that plays an important role in the data collection method. Automation of polygenic disorder analysis, as well as another machine learning algorithm. Design/methodology/approach: This paper provides extensive surveys of different analogies which have been used for the analysis of medical data, For the purpose of early detection of polygenic disorder. This paper takes into consideration methods such as J48, CART, SVMs and KNN square, this paper also conducts a formal surveying of all the studies, and provides a conclusion at the end. Findings: This surveying has been analyzed on several parameters within the poly-infected disorder diagnosis. It resembles that the classification algorithms of data collection plays an important role in the data collection method in Automation of polygenic disorder analysis, as well as another machine learning algorithm. Practical implications: This paper will help future researchers in the field of Healthcare, specifically in the domain of diabetes, to understand differences between classification algorithms. Originality/value: This paper will help in comparing machine learning algorithms by going through results and selecting the appropriate approach based on requirements.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals Supervised Learning Applied to Graduation Forecast of Industrial Engineering Students

2022 ◽  
Vol 11 (1) ◽  
pp. 325-337
Author(s):  
Natalia Gil ◽  
Marcelo Albuquerque ◽  
Gabriela de
Keyword(s):  
Machine Learning ◽  
High School ◽  
Logistic Regression ◽  
Supervised Learning ◽  
Grade Point Average ◽  
Engineering Students ◽  
Learning Algorithm ◽  
Industrial Engineering ◽  
Machine Learning Algorithm ◽  
Grade Point

<p style="text-align: justify;">The article aims to develop a machine-learning algorithm that can predict student’s graduation in the Industrial Engineering course at the Federal University of Amazonas based on their performance data. The methodology makes use of an information package of 364 students with an admission period between 2007 and 2019, considering characteristics that can affect directly or indirectly in the graduation of each one, being: type of high school, number of semesters taken, grade-point average, lockouts, dropouts and course terminations. The data treatment considered the manual removal of several characteristics that did not add value to the output of the algorithm, resulting in a package composed of 2184 instances. Thus, the logistic regression, MLP and XGBoost models developed and compared could predict a binary output of graduation or non-graduation to each student using 30% of the dataset to test and 70% to train, so that was possible to identify a relationship between the six attributes explored and achieve, with the best model, 94.15% of accuracy on its predictions.</p>

scholarly journals Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

2019 ◽  
Vol 8 (11) ◽  
pp. 4018-4022
Keyword(s):  
Machine Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Machine Learning Algorithm ◽  
Risk Modelling ◽  
Risk Monitoring ◽  
Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area

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