Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods

The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members.

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Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2020-0016 ◽

2020 ◽

pp. 1-9

Author(s):

Supun Nakandala ◽

Marta M. Jankowska ◽

Fatima Tuz-Zahra ◽

John Bellettiere ◽

Jordan A. Carlson ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Logistic Regression ◽

Random Forest ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Feature Engineering ◽

Free Living ◽

Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

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AS FORMAS DE VIOLÊNCIA À MULHER SOB A PROTEÇÃO DA LEI N° 11.340/06 (LEI MARIA DA PENHA)

COLLOQUIUM SOCIALIS ◽

10.5747/cs.2018.v02.nesp2.s0380 ◽

2018 ◽

Vol 2 (Especial 2) ◽

pp. 858-861

Author(s):

Tainara Andrea de Souza

Keyword(s):

Domestic Violence ◽

Family Violence ◽

Violence Against Women ◽

Domestic Violence Against Women

This article seeks to present and understand, in light of Law 11.340 / 06, this known as the Maria da Penha law, we will see in article seventh, the types of domestic violence against women, listed in its paragraphs, as well as the general considerations about such types of violence. The method used was the legal deductive, applying the interpretation of the legislation and doctrine. It is concluded that the main objective of this article is to demonstrate the forms of domestic and family violence against women, which are physical, psychological, sexual, property and moral violence.

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DETERMINANTS OF DOMESTIC PHYSICAL VIOLENCE AGAINST WOMEN IN TURKEY

Humanities & Social Sciences Reviews ◽

10.18510/hssr.2020.867 ◽

2021 ◽

Vol 8 (6) ◽

pp. 55-67

Author(s):

Ömer Alkan ◽

Şeyda Ünver

Keyword(s):

Domestic Violence ◽

Violence Against Women ◽

Physical Violence ◽

Intimate Partner ◽

Intimate Partners ◽

Probit Regression ◽

Data Set ◽

Factors Affecting ◽

Number Of Children ◽

Domestic Violence Against Women

Purpose of the study: This study aims to determine the factors affecting the exposure of women in Turkey 15 years of age and older to physical violence by their husband/intimate partner. Methodology: In this study, the micro-data set of the "Research on Domestic Violence against Women in Turkey" conducted by Hacettepe University Institute of Population Studies in 2008 and 2014 was used. In this data set, the data of 18518 women aged 15 and over were used, 11722 in 2008 and 6796 in 2014. Factors affecting women's physical violence were determined using binary logistic and probit regression analysis. This study focuses on the physical violence of the husband/partner, which is the most common type of domestic violence against women. Main Findings: The variables of survey year, region, education level, individual income, marital status, health status, the number of children, and being exposed to violence from first degree relatives are seen to be significant. According to the results obtained, the expected probability of exposure to physical violence women who were subjected to economic, verbal, and sexual violence by their husbands/intimate partners was more than 39.8%, 127.35%, and 83.68%, respectively. Applications of this study: The study outcome indicate that important steps to reduce domestic physical violence against women in Turkey should be taken. In order to prevent new cases of abuse, coordinated efforts to raise awareness of the problem of domestic physical violence against women will encourage action. Novelty/Originality of this study: In this study, factors affecting the exposure of women in Turkey, 15 years old and older, to physical violence by their husband/intimate partner were identified. In the study, the socio-demographic and economic characteristics of women and to what extent the various risk factors related to husband/intimate partners were critical for the women's exposure to physical violence.

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Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

Technology in Cancer Research & Treatment ◽

10.1177/1533033820909829 ◽

2020 ◽

Vol 19 ◽

pp. 153303382090982

Author(s):

Melek Akcay ◽

Durmus Etiz ◽

Ozer Celik ◽

Alaattin Ozen

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Naive Bayes ◽

Nasopharyngeal Cancer ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

Tumor Diameter ◽

Survival Prognosis ◽

Data Set

Background and Aim: Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning. Settings and Design: Original, retrospective. Materials and Methods: A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes. Results: In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%). Conclusion: Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

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Machine learning algorithm to predict delirium from emergency department data

10.1101/2021.02.19.21251956 ◽

2021 ◽

Author(s):

Sangil Lee ◽

Brianna Mueller ◽

W. Nick Street ◽

Ryan M. Carnahan

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Logistic Regression ◽

Random Forest ◽

Barthel Index ◽

Risk Model ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Sensitivity Threshold ◽

Mortality And Morbidity

AbstractIntroductionDelirium is a cerebral dysfunction seen commonly in the acute care setting. Delirium is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions.ObjectiveOur objective was to identify clinically valuable predictive models for prevalent delirium within the first 24 hours of hospitalization based on the available data by assessing the performance of logistic regression and a variety of machine learning models.MethodsThis was a retrospective cohort study to develop and validate a predictive risk model to detect delirium using patient data obtained around an ED encounter. Data from electronic health records for patients hospitalized from the ED between January 1, 2014, and December 31, 2019, were extracted. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded while hospitalized. The outcome measure of this study was delirium within one day of hospitalization determined by a positive DOSS or CAM assessment. We developed the model with and without the Barthel index for activity of daily living, since this was measured after hospital admission.ResultsThe area under the ROC curves for delirium ranged from .69 to .77 without the Barthel index. Random forest and gradient-boosted machine showed the highest AUC of .77. At the 90% sensitivity threshold, gradient-boosted machine, random forest, and logistic regression achieved a specificity of 35%. After the Barthel index was included, random forest, gradient-boosted machine, and logistic regression models demonstrated the best predictive ability with respective AUCs of .85 to .86.ConclusionThis study demonstrated the use of machine learning algorithms to identify the combination of variables that are predictive of delirium within 24 hours of hospitalization from the ED.

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Breast Cancer Detection Using Machine Learning Algorithms

International Journal of Computer Science and Mobile Computing ◽

10.47760/ijcsmc.2021.v10i11.002 ◽

2021 ◽

Vol 10 (11) ◽

pp. 4-11

Author(s):

Samer Hamed ◽

Abdelwadood Mesleh ◽

Abdullah Arabiyyat

Keyword(s):

Machine Learning ◽

Random Forest ◽

Cancer Detection ◽

Computer Aided Design ◽

Machine Learning Algorithms ◽

Breast Cancers ◽

Data Set ◽

Cad System ◽

Aided Design ◽

F Measure

This paper presents a computer-aided design (CAD) system that detects breast cancers (BCs). BC detection uses random forest, AdaBoost, logistic regression, decision trees, naïve Bayes and conventional neural networks (CNNs) classifiers, these machine learning (ML) based algorithms are trained to predicting BCs (malignant or benign) on BC Wisconsin data-set from the UCI repository, in which attribute clump thickness is used as evaluation class. The effectiveness of these ML algorithms are evaluated in terms of accuracy and F-measure; random forest outperformed the other classifiers and achieved 99% accuracy and 99% F-measure.

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The Contribution of CD148, CD180 and CD200 Combination in the Diagnosis of Chronic B-Cell Lymphoproliferative Disorders

Blood ◽

10.1182/blood-2021-150810 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3520-3520

Author(s):

Laurent Miguet ◽

Caroline Mayeur-Rousse ◽

Alice Eischen ◽

Anne-Cecile Galoisy ◽

Delphine C. M. Rolland ◽

...

Keyword(s):

Machine Learning ◽

Bone Marrow ◽

Flow Cytometry ◽

Logistic Regression ◽

Random Forest ◽

Expression Patterns ◽

Machine Learning Algorithms ◽

Relative Importance ◽

Predictive Values ◽

Molecular Features

Abstract Introduction: B-cell immunophenotype could be swiftly assessed by flow cytometry on blood samples or bone marrow aspirate specimens. It provides crucial information later refined with histologic, genetic and molecular features to assert accurate diagnosis of chronic B-cell lymphoproliferative disorders (B-CLPD). Besides Matutes score we identified additional useful markers, i.e. CD148 and CD180 to classify mantle cell lymphoma (MCL) and marginal zone lymphoma (MZL), respectively. Furthermore, CD200 is known to be highly expressed in chronic lymphoid leukemia (CLL) while absent in MCL. Hypothesis: The determination of CD148, CD180 and CD200 expression on B-cells by flow cytometry on blood samples and/or bone marrow aspirates could be a potent tool to accurately identify B-CLPD. We postulated the existence of the following specific expression patterns in B-CLPD: CD148 dim/CD180 dim/CD200 bright for CLL, CD148 dim/CD180 dim/CD200 dim for lymphoplasmocytic lymphoma (LPL), CD148 bright/CD180 dim/CD200 neg/dim for MCL and CD148 dim/CD180 bright/CD200 dim for MZL . Methods: In a prospective study we investigated the expression of CD148/CD180/CD200 on B-cells from 673 patients at the time of B-CLPD diagnosis in our hospital from 2014 to 2020. We analyzed 440 blood and 233 bone marrow aspirate specimens using a BD FACSCanto II flow cytometry instrument. Based solely on CD148/CD180/CD200 specific expression patterns we postulated a diagnosis of CLL, LPL, MCL or MZL. These postulated diagnoses were later confronted to the final diagnoses when all histologic, genetic and molecular features were finalized. Sensitivity, specificity, positive and negative predictive values of the expression profiles were determined. In addition, to investigate the relative importance of these three CD markers we then normalized their mean fluorescence intensities (MFI) and applied several supervised machine learning algorithms including Logistic Regression, Random Forest and Light Gradient Boosting Machine (LightGBM). Results: Out of the 673 clinical samples the CD148/CD180/CD200 expression patterns classified 212 specimens as CLL/SLL (30.8%), 160 as LPL (23.8%), 76 as MCL (11.28%) and 169 as MZL (25%). These diagnosis hypotheses were retrospectively compared to the final diagnoses based on all histologic, genetic and molecular features These diagnosis hypotheses of CLL, LPL, MCL and MZL were consistent with the final diagnosis in 583 out of the 617 corresponding cases (94%) with high positive and negative predictive values. The characteristics of the diagnosis accuracy are detailed in the table below. HCL and FL were not further investigated as their immunophenotype usually do not overlap with those of other B-CLPD. Seventeen out of 617 patients (17/617, 5.3%) did not displayed a clear CD148/CD180/CD200 pattern: 9 LPL, 4 CLL and 4 MZL. In sixteen patients (16/617, 5.0%) the diagnosis hypothesis based on this strategy was not confirmed after completion of the exploration including karyotype, MYD88 L265P mutational status, CCND1 overexpression and pathology explorations. We next investigated the relative importance of these 3 markers. We focused on MFI values of CD148, CD180 and CD200 and three categorical "positive or negative" markers (CD5, CD23, FMC7) that were assembled into a composite marker. After Cox-box normalization of CD148, CD180 and CD200 MFIs, a set of supervised machine learning algorithms including Logistic Regression, Random Forest and Light Gradient Boosting Machine (LightGBM) were applied to the cohort of CLL, LPL, MCL and MZL. We established that the highest diagnosis weights were obtained for CD200 in CLL, CD200 and CD148 in MCL (negatively and positively, respectively), CD180 in MZL. In LPL, CD148, CD180 and CD200 had the highest weights using LightGBM and Random Forest algorithms, while Logistic Regression determined that CD5 and CD23 had the highest (negative) weights. In conclusion, the determination of CD148/CD180/CD200 surface expression patterns by flow cytometry, along with morphology, allowed to assert an accurate diagnosis hypothesis in CLL, MCL, LPL and MZL with high positive and negative predictive values. Machine learning algorithms allowed to measure the relative importance of these markers, that could be of great help in case of discordant expression of the main diagnosis markers. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

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Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

European Heart Journal ◽

10.1093/eurheartj/ehab724.3067 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

M J Espinosa Pascual ◽

P Vaquero Martinez ◽

V Vaquero Martinez ◽

J Lopez Pais ◽

B Izquierdo Coronel ◽

...

Keyword(s):

Machine Learning ◽

Myocardial Infarction ◽

Support Vector Machine ◽

Logistic Regression ◽

Random Forest ◽

Obstructive Coronary Artery Disease ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

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ClickbaitTR: Dataset for clickbait detection from Turkish news sites and social media with a comparative analysis via machine learning algorithms

Journal of Information Science ◽

10.1177/01655515211007746 ◽

2021 ◽

pp. 016555152110077

Author(s):

Şura Genç ◽

Elif Surer

Keyword(s):

Machine Learning ◽

Social Media ◽

Logistic Regression ◽

Random Forest ◽

Short Term Memory ◽

Ensemble Classifier ◽

Machine Learning Algorithms ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Clickbait is a strategy that aims to attract people’s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset – ClickbaitTR – with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.

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Factors of Domestic Violence Against Women: Correlation of Women’s Rights and Vulnerability

Journal of Asian and African Studies ◽

10.1177/0021909616677373 ◽

2016 ◽

Vol 53 (2) ◽

pp. 285-296

Author(s):

Bonkoungou Zakaliyat ◽

A Sathiya Susuman

Keyword(s):

Domestic Violence ◽

Human Rights ◽

Logistic Regression ◽

Violence Against Women ◽

Quantitative Data ◽

Sociocultural Factors ◽

Constitutional Rights ◽

Multivariate Logistic Regression ◽

Health And Human Rights ◽

Domestic Violence Against Women

Many studies have looked into domestic violence but very few have considered women’s knowledge and perception about their rights. This study aims to examine the main sociocultural factors behind domestic violence against women with an emphasis on the power of the knowledge and perception of the women about their international and constitutional rights. Quantitative data collected in 2015 in the regions of East and Southwest Burkina Faso is used in this paper. Multivariate logistic regression is implemented to take into consideration the net effects of each factor when controlling the effects of other covariates. Results of this paper can be used to implement actions against domestic violence in the zone of intervention of the Program of Sexual Health and Human Rights project.

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