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Oğur NB, Kotan M, Balta D, Yavuz BÇ, Oğur YS, Yuvacı HU, Yazıcı E. Detection of depression and anxiety in the perinatal period using Marine Predators Algorithm and kNN. Comput Biol Med 2023; 161:107003. [PMID: 37224599 DOI: 10.1016/j.compbiomed.2023.107003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/19/2023] [Accepted: 05/02/2023] [Indexed: 05/26/2023]
Abstract
Undiagnosed prenatal anxiety and depression have the potential to worsen and have an adverse effect on both the mother and the infant. Although the diagnosis is made by specialist doctors, it is unclear which parameters are more effective. Especially in medicine, it is crucial to diagnose disease with high accuracy. For this reason, in this study, a questionnaire study was first conducted on pregnant women, and real original data were collected. Then, the Marine Predators Algorithm (MPA), one of the current metaheuristic algorithms inspired by nature, was combined with K-Nearest Neighbors (kNN) to determine high-priority features in the collected data. As a result, five of the 147 features selected by the proposed method were determined as high priority and approved by the doctors. In addition, the proposed method is compared with the Chi-square method, which is one of the filter-based feature selection methods. Thanks to the proposed feature selection method based on MPA and kNN, it has been observed that the classification gives more successful results in a shorter time with 98.11% success, and the model supports the diagnosis stage of the doctors.
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Affiliation(s)
- Nur Banu Oğur
- Sakarya University, Faculty of Computer and Information Sciences, Department of Computer Engineering, Sakarya, Turkey.
| | - Muhammed Kotan
- Sakarya University, Faculty of Computer and Information Sciences, Department of Information Systems Engineering, Sakarya, Turkey
| | - Deniz Balta
- Sakarya University, Faculty of Computer and Information Sciences, Department of Software Engineering, Sakarya, Turkey
| | - Burcu Çarklı Yavuz
- Sakarya University, Faculty of Computer and Information Sciences, Department of Information Systems Engineering, Sakarya, Turkey
| | - Yavuz Selim Oğur
- Sakarya University, Faculty of Medicine, Department of Psychiatry, Sakarya, Turkey
| | - Hilal Uslu Yuvacı
- Sakarya University, Faculty of Medicine, Department of Obstetrics and Gynecology, Sakarya, Turkey
| | - Esra Yazıcı
- Sakarya University, Faculty of Medicine, Department of Psychiatry, Sakarya, Turkey
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Jin HY, Cutumisu M. Predicting pre-service teachers' computational thinking skills using machine learning classifiers. Educ Inf Technol (Dordr) 2023; 28:1-21. [PMID: 36846494 PMCID: PMC9939859 DOI: 10.1007/s10639-023-11642-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT training environment as well as compared and contrasted the predictive capacity of four supervised machine learning algorithms in classifying the CT skills of pre-service teachers using log data and survey data. First, the results show that Decision Tree outperformed K-Nearest Neighbors, Logistic Regression, and Naive Bayes in predicting pre-service teachers' CT skills. Second, the participants' time spent on CT training, prior CT skills, and perceptions of difficulty regarding the learning content were the top three important predictors in this model.
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Affiliation(s)
- Hao-Yue Jin
- Centre for Research in Applied Measurement and Evaluation, Department of Educational Psychology, Faculty of Education, University of Alberta, 6-102 Education Centre North, Edmonton, T6G 2G5 Canada
| | - Maria Cutumisu
- Centre for Research in Applied Measurement and Evaluation, Department of Educational Psychology, Faculty of Education, University of Alberta, 6-102 Education Centre North, Edmonton, T6G 2G5 Canada
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González-Pardo J, Ceballos-Santos S, Manzanas R, Santibáñez M, Fernández-Olmo I. Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain. Sci Total Environ 2022; 823:153786. [PMID: 35151743 PMCID: PMC8828445 DOI: 10.1016/j.scitotenv.2022.153786] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/31/2022] [Accepted: 02/06/2022] [Indexed: 05/19/2023]
Abstract
In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2, O3, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013-2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (KNN)), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013-2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx patterns with a maximum increase of 23.9%. The slight reduction in PM10 (-4.1%) and PM2.5 levels (-2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.
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Affiliation(s)
- Jaime González-Pardo
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
| | - Sandra Ceballos-Santos
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
| | - Rodrigo Manzanas
- Santander Meteorology Group, Dpto. De Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander 39005, Spain.
| | - Miguel Santibáñez
- Department of Nursing, Global Health Research Group, Universidad de Cantabria, Avda. Valdecilla s/n, 39008 Santander, Cantabria, Spain; Research Nursing Group, IDIVAL, Calle Cardenal Herrera Oria s/n, 39011 Santander, Cantabria, Spain.
| | - Ignacio Fernández-Olmo
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
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González-Pardo J, Ceballos-Santos S, Manzanas R, Santibáñez M, Fernández-Olmo I. Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain. Sci Total Environ 2022. [PMID: 35151743 DOI: 10.5281/zenodo.5655326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
In response to the COVID-19 pandemic, governments declared severe restrictions throughout 2020, presenting an unprecedented scenario of reduced anthropogenic emissions of air pollutants derived mainly from traffic sources. To analyze the effect of these restrictions derived from COVID-19 pandemic on air quality levels, relative changes in NO, NO2, O3, PM10 and PM2.5 concentrations were calculated at urban traffic sites in the most populated Spanish cities over different periods with distinct restrictions in 2020. In addition to the changes calculated with respect to the observed air pollutant levels of previous years (2013-2019), relative changes were also calculated using predicted pollutant levels for the different periods over 2020 on a business-as-usual scenario using Multiple Linear Regression (MLR) models with meteorological and seasonal predictors. MLR models were selected among different data mining techniques (MLR, Random Forest (RF), K-Nearest Neighbors (KNN)), based on their higher performance and accuracy obtained from a leave-one-year-out cross-validation scheme using 2013-2019 data. A q-q mapping post-correction was also applied in all cases in order to improve the reliability of the predictions to reproduce the observed distributions and extreme events. This approach allows us to estimate the relative changes in the studied air pollutants only due to COVID-19 restrictions. The results obtained from this approach show a decreasing pattern for NOx, with the largest reduction in the lockdown period above -50%, whereas the increase observed for O3 contrasts with the NOx patterns with a maximum increase of 23.9%. The slight reduction in PM10 (-4.1%) and PM2.5 levels (-2.3%) during lockdown indicates a lower relationship with traffic sources. The developed methodology represents a simple but robust framework for exploratory analysis and intervention detection in air quality studies.
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Affiliation(s)
- Jaime González-Pardo
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
| | - Sandra Ceballos-Santos
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
| | - Rodrigo Manzanas
- Santander Meteorology Group, Dpto. De Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander 39005, Spain.
| | - Miguel Santibáñez
- Department of Nursing, Global Health Research Group, Universidad de Cantabria, Avda. Valdecilla s/n, 39008 Santander, Cantabria, Spain; Research Nursing Group, IDIVAL, Calle Cardenal Herrera Oria s/n, 39011 Santander, Cantabria, Spain.
| | - Ignacio Fernández-Olmo
- Department of Chemical and Biomolecular Engineering, Universidad de Cantabria, Av. de Los Castros s/n, 39005 Santander, Cantabria, Spain.
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Moral-Rubio C, Balugo P, Fraile-Pereda A, Pytel V, Fernández-Romero L, Delgado-Alonso C, Delgado-Álvarez A, Matias-Guiu J, Matias-Guiu JA, Ayala JL. Application of Machine Learning to Electroencephalography for the Diagnosis of Primary Progressive Aphasia: A Pilot Study. Brain Sci 2021; 11:1262. [PMID: 34679327 DOI: 10.3390/brainsci11101262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 08/22/2021] [Accepted: 09/23/2021] [Indexed: 11/17/2022] Open
Abstract
Background. Primary progressive aphasia (PPA) is a neurodegenerative syndrome in which diagnosis is usually challenging. Biomarkers are needed for diagnosis and monitoring. In this study, we aimed to evaluate Electroencephalography (EEG) as a biomarker for the diagnosis of PPA. Methods. We conducted a cross-sectional study with 40 PPA patients categorized as non-fluent, semantic, and logopenic variants, and 20 controls. Resting-state EEG with 32 channels was acquired and preprocessed using several procedures (quantitative EEG, wavelet transformation, autoencoders, and graph theory analysis). Seven machine learning algorithms were evaluated (Decision Tree, Elastic Net, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gaussian Naive Bayes, and Multinomial Naive Bayes). Results. Diagnostic capacity to distinguish between PPA and controls was high (accuracy 75%, F1-score 83% for kNN algorithm). The most important features in the classification were derived from network analysis based on graph theory. Conversely, discrimination between PPA variants was lower (Accuracy 58% and F1-score 60% for kNN). Conclusions. The application of ML to resting-state EEG may have a role in the diagnosis of PPA, especially in the differentiation from controls. Future studies with high-density EEG should explore the capacity to distinguish between PPA variants.
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Abstract
Sleep Apnea is a sleep disorder which causes stop in breathing for a short duration of time that happens to human beings and animals during sleep. Electroencephalogram (EEG) plays a vital role in detecting the sleep apnea by sensing and recording the brain's activities. The EEG signal dataset is subjected to filtering by using Infinite Impulse Response Butterworth Band Pass Filter and Hilbert Huang Transform. After pre-processing, the filtered EEG signal is manipulated for sub-band separation and it is fissioned into five frequency bands such as Gamma, Beta, Alpha, Theta, and Delta. This work employs features such as energy, entropy, and variance which are computed for each frequency band obtained from the decomposed EEG signals. The selected features are imported for the classification process by using machine learning classifiers including Support Vector Machine (SVM) with Kernel Functions, K-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). The performance measures such as accuracy, sensitivity, and specificity are computed and analyzed for each classifier and it is inferred that the Support Vector Machine based classification of sleep apnea produces promising results.
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Affiliation(s)
- V Vimala
- Department of Computer Science and Engineering, Chennai Institute of Technology, Kundrathur, Chennai, Tamil Nadu, India.
| | - K Ramar
- Department of Computer Science and Engineering, Einstein College of Engineering, Tirunelveli, Tamil Nadu, India
| | - M Ettappan
- Department of Electrical and Electronics Engineering, Chennai Institute of Technology, Kundrathur, Chennai, Tamil Nadu, India
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Abstract
Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.
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Affiliation(s)
- Shobeir Fakhraei
- Medical Image Analysis Laboratory, Department of Radiology, Henry Ford Health System, Detroit, MI 48202, USA ; Department of Computer Science, University of Maryland, College Park, MD 20740, USA
| | - Hamid Soltanian-Zadeh
- Medical Image Analysis Laboratory, Department of Radiology, Henry Ford Health System, Detroit, MI 48202, USA ; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 14395, Iran
| | - Farshad Fotouhi
- College of Engineering, Wayne State University, Detroit, MI 48202, USA
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