scholarly journals Comparison of Machine Learning algorithms for the Burnout projection

2021 ◽  
pp. 1-8
Author(s):  
Luis Rey Lara-González ◽  
Martha Angélica Delgado-Luna ◽  
Beatriz Elena De León-Galván ◽  
José Carlos Venegas-Guerrero
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Cross Sectional ◽  
Psychology Education ◽  
Risk Detection ◽  
Preventive Actions ◽  
Student Burnout ◽  
Burnout Risk ◽  
Virtual Resources ◽  
Descriptive Method

The present study aims to carry out a projection of student burnout risk detection in young university students using Machine Learning technics (Neuronal Networks, KNN, SVM, Random Forest). A descriptive method was proposed, with a cross-sectional and stratified design in which a sample of 791 students from 4 different universities. This study opens up an innovative field of research by integrating resources from psychological evaluation and virtual resources, in addition, it would allow the generation of preventive actions to treat various implications of Burnout in school dropout and low academic performance through the analysis of information and the generation of algorithms that allow the projection of burnout risk. Due to the combination of experience of professionals in psychology, education and engineering, as well as the contribution to the projection of a syndrome that affects students, makes this article an innovative proposal.

PREDICTING SOFTWARE CHANGE IN AN OPEN SOURCE SOFTWARE USING MACHINE LEARNING ALGORITHMS

2013 ◽  
Vol 20 (06) ◽  
pp. 1350025
Author(s):  
RUCHIKA MALHOTRA ◽  
ANKITA JAIN BANSAL
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Open Source Software ◽  
Roc Analysis ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Preventive Actions ◽  
Change Proneness ◽  
The Relationship

Due to various reasons such as ever increasing demands of the customer or change in the environment or detection of a bug, changes are incorporated in a software. This results in multiple versions or evolving nature of a software. Identification of parts of a software that are more prone to changes than others is one of the important activities. Identifying change prone classes will help developers to take focused and timely preventive actions on the classes of the software with similar characteristics in the future releases. In this paper, we have studied the relationship between various object oriented (OO) metrics and change proneness. We collected a set of OO metrics and change data of each class that appeared in two versions of an open source dataset, 'Java TreeView', i.e., version 1.1.6 and version 1.0.3. Besides this, we have also predicted various models that can be used to identify change prone classes, using machine learning and statistical techniques and then compared their performance. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the models predicted using both machine learning and statistical methods demonstrate good performance in terms of predicting change prone classes. Based on the results, it is reasonable to claim that quality models have a significant relevance with OO metrics and hence can be used by researchers for early prediction of change prone classes.

scholarly journals A machine learning approach to identify psychological factors driving vaccine hesitancy in high income countries

2021 ◽  
Author(s):  
Tania Lincoln ◽  
Björn Schlier ◽  
Felix Strakeljahn ◽  
Brandon Gaudiano ◽  
Suzanne So ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perceived Risk ◽  
Social Rank ◽  
Female Gender ◽  
Machine Learning Algorithms ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Cross Sectional ◽  
Conspiracy Beliefs ◽  
Cross Sectional Design

Abstract Understanding factors driving vaccine hesitancy is crucial to vaccination success. We surveyed adults (N=2510) from February to March 2021 across five sites (Australia=502, Germany=516, Hong Kong=445, UK=512, USA=535) using a cross-sectional design and stratified quota sampling for age, sex, and education. We assessed willingness to take a vaccine and a comprehensive set of putative predictors. Predictive power was analysed with regression analyses and machine learning algorithms. Only 57.4% of the participants indicated that they would definitely or probably get vaccinated. A parsimonious machine-learning model could identify vaccine hesitancy with high accuracy (i.e. 83% sensitivity and 82% specificity) using 10 variables only. The most relevant predictors were vaccination conspiracy beliefs, paranoid concerns related to the pandemic, COVID anxiety, high perceived risk of infection, low perceived social rank, lower age, and female gender. Campaigns seeking to increase vaccine uptake need to take mistrust as the main driver of vaccine hesitancy into account.

scholarly journals Estimating Bus Cross-Sectional Flow Based on Machine Learning Algorithm Combined with Wi-Fi Probe Technology

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 844
Author(s):  
Ting-Zhao Chen ◽  
Yan-Yan Chen ◽  
Jian-Hui Lai
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Transport Systems ◽  
Support Vector ◽  
Surveillance Video ◽  
K Nearest Neighbor ◽  
Cross Sectional ◽  
Passenger Flow

With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future.

Predicting Risk of Suicide Attempts Over Time Through Machine Learning

2017 ◽  
Vol 5 (3) ◽  
pp. 457-469 ◽  
Author(s):  
Colin G. Walsh ◽  
Jessica D. Ribeiro ◽  
Joseph C. Franklin
Keyword(s):  
Machine Learning ◽  
Suicide Attempt ◽  
Suicide Attempts ◽  
Machine Learning Algorithms ◽  
Brier Score ◽  
Medical Database ◽  
Expert Review ◽  
Risk Detection ◽  
Self Injury ◽  
Over Time

Traditional approaches to the prediction of suicide attempts have limited the accuracy and scale of risk detection for these dangerous behaviors. We sought to overcome these limitations by applying machine learning to electronic health records within a large medical database. Participants were 5,167 adult patients with a claim code for self-injury (i.e., ICD-9, E95x); expert review of records determined that 3,250 patients made a suicide attempt (i.e., cases), and 1,917 patients engaged in self-injury that was nonsuicidal, accidental, or nonverifiable (i.e., controls). We developed machine learning algorithms that accurately predicted future suicide attempts (AUC = 0.84, precision = 0.79, recall = 0.95, Brier score = 0.14). Moreover, accuracy improved from 720 days to 7 days before the suicide attempt, and predictor importance shifted across time. These findings represent a step toward accurate and scalable risk detection and provide insight into how suicide attempt risk shifts over time.

scholarly journals Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder based on cerebral sMRI, rs-fMRI, and EEG: protocols for three systematic reviews and meta-analyses

2021 ◽  
Author(s):  
Amir Valizadeh ◽  
Mana Moassefi ◽  
Amin Nakhostin-Ansari ◽  
Iman Menbari Oskoie ◽  
Soheil Heidari Some'eh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Diagnostic Accuracy ◽  
Learning Algorithms ◽  
Grey Literature ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Cross Sectional ◽  
Medicine Research

Objective: To determine the diagnostic accuracy of the applied machine learning algorithms for the diagnosis of autism spectrum disorder (ASD) based on structural magnetic resonance imaging (sMRI), resting-state functional MRI (rs-fMRI), and electroencephalography (EEG). Methods: We will include cross-sectional studies (both single-gates and two-gates) that have evaluated the diagnostic accuracy of machine learning algorithms on the sMRI data of ASD patients regardless of age, sex, and ethnicity. On the 22nd of May 2021, we searched Embase, MEDLINE, APA PsycINFO, IEEE Xplore, Scopus, and Web of Science for eligible studies. We also searched grey literature within various sources. We will use an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Data will be synthesized using the relatively new Split Component Synthesis (SCS) method. We plan to assess heterogeneity using the I2 statistics and assess publication bias using trim and fill tests combined with ln DOR. Certainty of evidence will be assessed using the GRADE approach for diagnostic studies. Funding: These studies are funded by Sports Medicine Research Center, Tehran, Iran. Registration: PROSPERO submission IDs: 262575, 262825, and 262831.

scholarly journals Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053603
Author(s):  
Lotus McDougal ◽  
Nabamallika Dehingia ◽  
Nandita Bhan ◽  
Abhishek Singh ◽  
Julian McAuley ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sexual Violence ◽  
Thematic Analysis ◽  
Health Knowledge ◽  
Machine Learning Algorithms ◽  
Paper Machine ◽  
Cross Sectional ◽  
The Past ◽  
Modifiable Factors

ObjectivesSexual violence against women is pervasive in India. Most of this violence is experienced in the context of marriage, and rates of marital sexual violence (MSV) have been relatively stagnant over the past decade. This paper machine learning algorithms paired with qualitative thematic analysis to identify new and potentially modifiable factors influencing MSV in India.Design, setting and participantsThis cross-sectional analysis of secondary data used data from in-person interviews with ever-married women aged 15–49 who responded to gender-based violence questions in the nationally representative 2015–2016 National Family Health Survey (N=66 013), collected between 20 January 2015 and 4 December 2016. Analyses included iterative thematic analysis (L-1 regularised regression followed by iterative qualitative thematic coding of L-2 regularised regression results) and neural network modelling.Outcome measureParticipants reported their experiences of sexual violence perpetrated by their current (or most recent) husband in the previous 12 months. These responses were aggregated into any vs no recent MSV.ResultsNearly 7% of women experienced MSV in the past 12 months. Major themes associated with MSV through iterative thematic analysis included experiences of/exposure to violence, sexual behaviour, decision making and freedom of movement, sociodemographics, access to media, health knowledge, health system interaction, partner control, economic agency, reproductive and maternal history, and health status. A neural network model identified variables that largely corresponded to these themes.ConclusionsThis analysis identified several themes that may be promising avenues to identify and support women experiencing MSV, and to mitigate these traumatic experiences. In particular, amplifying screening activities at health encounters, especially among women who appear to have compromised health or restricted agency, may enable a greater number of women access to essential physical and emotional support services, and merits further consideration.

scholarly journals CN4 - COMPARISON OF MACHINE LEARNING ALGORITHMS FOR THE PREDICTION OF MISSING CROSS-SECTIONAL COST DATA

2018 ◽  
Vol 21 ◽  
pp. S4
Author(s):  
J. Rueda ◽  
C.F. Valencia ◽  
C.D. Mullins ◽  
E. Onukwugha ◽  
M. Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Cost Data ◽  
Cross Sectional

scholarly journals Prediction of Suicidal Ideation among Korean Adults Using Machine Learning: A Cross-Sectional Study

2020 ◽  
Vol 17 (4) ◽  
pp. 331-340
Author(s):  
Bumjo Oh ◽  
Je-Yeon Yun ◽  
Eun Chong Yeo ◽  
Dong-Hoi Kim ◽  
Jin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Primary Care Physicians ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Receiver Operating Curve ◽  
Training Dataset ◽  
Service Needs ◽  
Cross Sectional ◽  
Korean Adults

Objective Suicidal ideation (SI) precedes actual suicidal event. Thus, it is important for the prevention of suicide to screen the individuals with SI. This study aimed to identify the factors associated with SI and to build prediction models in Korean adults using machine learning methods.Methods The 2010–2013 dataset of the Korea National Health and Nutritional Examination Survey was used as the training dataset (n=16,437), and the subset collected in 2015 was used as the testing dataset (n=3,788). Various machine learning algorithms were applied and compared to the conventional logistic regression (LR)-based model.Results Common risk factors for SI included stress awareness, experience of continuous depressive mood, EQ-5D score, depressive disorder, household income, educational status, alcohol abuse, and unmet medical service needs. The prediction performances of the machine learning models, as measured by the area under receiver-operating curve, ranged from 0.794 to 0.877, some of which were better than that of the conventional LR model (0.867). The Bayesian network, LogitBoost with LR, and ANN models outperformed the conventional LR model.Conclusion A machine learning-based approach could provide better SI prediction performance compared to a conventional LR-based model. These may help primary care physicians to identify patients at risk of SI and will facilitate the early prevention of suicide.

scholarly journals Prediction of Physical Frailty in Orthogeriatric Patients Using Sensor Insole–Based Gait Analysis and Machine Learning Algorithms: Cross-sectional Study

10.2196/32724 ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. e32724
Author(s):  
Moritz Kraus ◽  
Maximilian Michael Saller ◽  
Sebastian Felix Baumbach ◽  
Carl Neuerburg ◽  
Ulla Cordula Stumpf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gait Analysis ◽  
Physical Performance ◽  
Learning Algorithms ◽  
Cross Sectional Study ◽  
Machine Learning Algorithms ◽  
Sectional Study ◽  
Physical Frailty ◽  
Cross Sectional ◽  
Gait Parameters

Background Assessment of the physical frailty of older patients is of great importance in many medical disciplines to be able to implement individualized therapies. For physical tests, time is usually used as the only objective measure. To record other objective factors, modern wearables offer great potential for generating valid data and integrating the data into medical decision-making. Objective The aim of this study was to compare the predictive value of insole data, which were collected during the Timed-Up-and-Go (TUG) test, to the benchmark standard questionnaire for sarcopenia (SARC-F: strength, assistance with walking, rising from a chair, climbing stairs, and falls) and physical assessment (TUG test) for evaluating physical frailty, defined by the Short Physical Performance Battery (SPPB), using machine learning algorithms. Methods This cross-sectional study included patients aged >60 years with independent ambulation and no mental or neurological impairment. A comprehensive set of parameters associated with physical frailty were assessed, including body composition, questionnaires (European Quality of Life 5-dimension [EQ 5D 5L], SARC-F), and physical performance tests (SPPB, TUG), along with digital sensor insole gait parameters collected during the TUG test. Physical frailty was defined as an SPPB score≤8. Advanced statistics, including random forest (RF) feature selection and machine learning algorithms (K-nearest neighbor [KNN] and RF) were used to compare the diagnostic value of these parameters to identify patients with physical frailty. Results Classified by the SPPB, 23 of the 57 eligible patients were defined as having physical frailty. Several gait parameters were significantly different between the two groups (with and without physical frailty). The area under the receiver operating characteristic curve (AUROC) of the TUG test was superior to that of the SARC-F (0.862 vs 0.639). The recursive feature elimination algorithm identified 9 parameters, 8 of which were digital insole gait parameters. Both the KNN and RF algorithms trained with these parameters resulted in excellent results (AUROC of 0.801 and 0.919, respectively). Conclusions A gait analysis based on machine learning algorithms using sensor soles is superior to the SARC-F and the TUG test to identify physical frailty in orthogeriatric patients.

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