scholarly journals Analysis of Traffic Accident Features and Crash Severity Prediction

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Recall Performance ◽  
Model Performance ◽  
Sampling Technique ◽  
Performance Measure ◽  
Road Accident ◽  
Vehicle Crashes ◽  
Data Set ◽  
Severity Prediction

Vehicle crashes occur because of numerous factors. It leads to loss of lives and permanent incapacity. The budgetary expenses of both individuals as well as for the nation are influenced by vehicle crashes. According to Road accident statistics, a total of 464910 road accidents were reported in India, claiming 1,47,913 lives and causing injuries to 4,70,975 persons every year. In this work, the UK data set sourced from Kaggle is used. For the study, 17 attributes and 35k records of the year 2015 are considered. The data set is imbalanced, so to balance out the data, the over-sampling technique is used. Random Forest, Decision tree, Logistic Regression, and Gradient Naïve Bayes algorithms are used to predict the severity of Accidents. To evaluate the model, performance measures like Accuracy, Precision, Recall, F1-Score are used. When Accuracy, Precision, F1-Score performance measure is considered Random Forest yielded the best result. When Recall performance measure is used, Random forest for Fatal, Decision Trees for Serious, Logistic regression for Slight yielded the best result.

scholarly journals Predicting voluntary turnover through human resources database analysis

2018 ◽  
Vol 41 (1) ◽  
pp. 96-112 ◽  
Author(s):  
Evy Rombaut ◽  
Marie-Anne Guerry
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Human Resources ◽  
Real Life ◽  
Model Performance ◽  
Voluntary Turnover ◽  
Private Company ◽  
Data Set ◽  
Content Type ◽  
Individual Level

Purpose This paper aims to question whether the available data in the human resources (HR) system could result in reliable turnover predictions without supplementary survey information. Design/methodology/approach A decision tree approach and a logistic regression model for analysing turnover were introduced. The methodology is illustrated on a real-life data set of a Belgian branch of a private company. The model performance is evaluated by the area under the ROC curve (AUC) measure. Findings It was concluded that data in the personnel system indeed lead to valuable predictions of turnover. Practical implications The presented approach brings determinants of voluntary turnover to the surface. The results yield useful information for HR departments. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk. Originality/value The study of a data-driven approach for turnover investigation has not been done so far.

scholarly journals Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia

2021 ◽  
Vol 6 (2) ◽  
pp. 120-129
Author(s):  
Nadhif Ikbar Wibowo ◽  
Tri Andika Maulana ◽  
Hamzah Muhammad ◽  
Nur Aini Rakhmawati
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Supervised Learning ◽  
Support Vector ◽  
Data Set ◽  
Logistic Regression Classifier

Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score 0.503583. SVM is the best performing classifier.

scholarly journals An empirical investigation of alternative semi-supervised segmentation methodologies

2019 ◽  
Vol 115 (3/4) ◽  
Author(s):  
Douw G. Breed ◽  
Tanja Verster
Keyword(s):  
Logistic Regression ◽  
Model Performance ◽  
Predictive Modelling ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Supervised Segmentation ◽  
Improved Performance ◽  
Validation Set ◽  
Combination Approach

Segmentation of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main types of segmentation and examples of improved performance of predictive models exist for both approaches. However, both focus on a single aspect – either target separation or independent variable distribution – and combining them may deliver better results. This combination approach is called semi-supervised segmentation. Our objective was to explore four new semi-supervised segmentation techniques that may offer alternative strengths. We applied these techniques to six data sets from different domains, and compared the model performance achieved. The original semi-supervised segmentation technique was the best for two of the data sets (as measured by the improvement in validation set Gini), but others outperformed for the other four data sets. Significance: We propose four newly developed semi-supervised segmentation techniques that can be used as additional tools for segmenting data before fitting a logistic regression. In all comparisons, using semi-supervised segmentation before fitting a logistic regression improved the modelling performance (as measured by the Gini coefficient on the validation data set) compared to using unsegmented logistic regression.

scholarly journals Development of nomograms to assess the risk of clinical outcome

2019 ◽  
Vol 21 (2) ◽  
pp. 114-121
Author(s):  
A A Korneenkov ◽  
S G Kuzmin ◽  
V B Dergachev ◽  
D N Borisov
Keyword(s):  
Logistic Regression ◽  
Clinical Outcome ◽  
Calibration Curve ◽  
Bootstrap Method ◽  
Model Performance ◽  
Outcome Variable ◽  
Software Environment ◽  
R Software ◽  
Data Set ◽  
Virtual Data

A methodology is presented for developing nomograms for assessing and stratifying the risk of a clinical outcome based on the created virtual data set using the R software environment. The virtual data set included input numerical and factor variables (variable types correspond to the R software documentation) and outcome. For quantitative variables, descriptive statistics were calculated at all levels of the outcome variable, and mosaic diagrams were constructed for factor variables. As a model that describes the association of input variables with the outcome, a logistic regression model was used. A bootstrap method was applied to validate and evaluate the model performance. The calculated validity indicators showed an acceptable discriminatory ability of the predictive model. The statistical calibration demonstrated the proximity of the model’s calibration curve to the ideal calibration curve. Based on the logistic regression coefficients, a nomogram was constructed using which the risk value of a specific outcome was calculated for each subject (patient). It is shown that with the help of the presented technique it is possible to stratify patients effectively by the risk of an adverse outcome, thus adequately altering the diagnosis and treatment tactics. The use of a nomogram greatly simplifies risk assessment and can be used in paper form as a supplement to the patient examination protocol. The article contains the codes of the R programming language with explanations.

scholarly journals Comparison of Regression and Machine Learning Methods in Depression Forecasting Among Home-Based Elderly Chinese: A Community Based Study

2022 ◽  
Vol 12 ◽  
Author(s):  
Shaowu Lin ◽  
Yafei Wu ◽  
Ya Fang
Keyword(s):  
Machine Learning ◽  
Life Satisfaction ◽  
Logistic Regression ◽  
Random Forest ◽  
Cognitive Ability ◽  
Characteristic Curve ◽  
Model Performance ◽  
Predictive Performance ◽  
Home Based ◽  
Elderly Chinese

BackgroundDepression is highly prevalent and considered as the most common psychiatric disorder in home-based elderly, while study on forecasting depression risk in the elderly is still limited. In an endeavor to improve accuracy of depression forecasting, machine learning (ML) approaches have been recommended, in addition to the application of more traditional regression approaches.MethodsA prospective study was employed in home-based elderly Chinese, using baseline (2011) and follow-up (2013) data of the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study. We compared four algorithms, including the regression-based models (logistic regression, lasso, ridge) and ML method (random forest). Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance, we used the area under the receiver operating characteristic curve (AUC).ResultsThe mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.795, 0.794, 0.794, and 0.769, respectively. The main determinants were life satisfaction, self-reported memory, cognitive ability, ADL (activities of daily living) impairment, CESD-10 score. Life satisfaction increased the odds ratio of a future depression by 128.6% (logistic), 13.8% (lasso), and 13.2% (ridge), and cognitive ability was the most important predictor in random forest.ConclusionsThe three regression-based models and one ML algorithm performed equally well in differentiating between a future depression case and a non-depression case in home-based elderly. When choosing a model, different considerations, however, such as easy operating, might in some instances lead to one model being prioritized over another.

scholarly journals Student Academic Performance Prediction under Various Machine Learning Classification Algorithms

2021 ◽  
Vol 9 (11) ◽  
pp. 221-237
Author(s):  
M. Nirmala
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Academic Performance ◽  
Random Forest ◽  
Decision Tree ◽  
Problem Definition ◽  
Classification Algorithms ◽  
Data Set ◽  
Machine Learning Classification ◽  
Student Academic Performance

Abstract: Data Mining in Educational System has increased tremendously in the past and still increasing in present era. This study focusses on the academic stand point and the performance of the student is evaluated by various parameters such as Scholastic Features, Demographic Features and Emotional Features are carried out. Various Machine learning methodologies are adopted to extract the masked knowledge from the educational data set provided, which helps in identifying the features giving more impact to the student academic performance and there by knowing the impacting features, helps us to predict deeper insights about student performance in academics. Various Machine learning workflow starting from problem definition to Model Prediction has been carried out in this study. The supervised learning methodology has been adopted and various Feature engineering methods has been adopted to make the ML model appropriate for training and evaluation. It is a prediction problem and various Classification algorithms such as Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree modelling has been done to fit the student data appropriately. Keywords: Scholastic, Demographic, Emotional, Logistic Regression, Random Forest, SVM, KNN, XGBOOST, Decision Tree.

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

2021 ◽  
Vol 4 (4) ◽  
pp. 77
Author(s):  
Md. Murad Hossain ◽  
Md. Asadullah ◽  
Abidur Rahaman ◽  
Md. Sipon Miah ◽  
M. Zahid Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domestic Violence ◽  
Logistic Regression ◽  
Random Forest ◽  
Family Violence ◽  
Violence Against Women ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Domestic Violence Against Women ◽  
Women And Children

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.

scholarly journals Modeling Road Accident Severity with Comparisons of Logistic Regression, Decision Tree and Random Forest

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 270 ◽  
Author(s):  
Mu-Ming Chen ◽  
Mu-Chen Chen
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Sensitivity And Specificity ◽  
Prediction Models ◽  
Regression Tree ◽  
Positive Influence ◽  
Road Accident ◽  
Prediction Performance ◽  
Classification And Regression Tree ◽  
Accident Severity

To reduce the damage caused by road accidents, researchers have applied different techniques to explore correlated factors and develop efficient prediction models. The main purpose of this study is to use one statistical and two nonparametric data mining techniques, namely, logistic regression (LR), classification and regression tree (CART), and random forest (RF), to compare their prediction capability, identify the significant variables (identified by LR) and important variables (identified by CART or RF) that are strongly correlated with road accident severity, and distinguish the variables that have significant positive influence on prediction performance. In this study, three prediction performance evaluation measures, accuracy, sensitivity and specificity, are used to find the best integrated method which consists of the most effective prediction model and the input variables that have higher positive influence on accuracy, sensitivity and specificity.

Predictive Modeling for Imbalanced Big Data in SAS Enterprise Miner and R

2018 ◽  
Vol 1 (2) ◽  
pp. 83-108
Author(s):  
Son Nguyen ◽  
Alan Olinsky ◽  
John Quinn ◽  
Phyllis Schumacher
Keyword(s):  
Logistic Regression ◽  
Predictive Models ◽  
Rare Event ◽  
Sampling Technique ◽  
Occurrence Rate ◽  
Target Variable ◽  
Data Set ◽  
Resampling Methods ◽  
Home Mortgage ◽  
The Impact

There have been a variety of predictive models capable of handling binary targets, ranging from traditional logistic regression to modern neural networks. However, when the target variable represents a rare event, these models might not be appropriate as they assume that the distribution in the target variable is balanced. In this article, the impact of multiple resampling methods on conventional predictive models is studied. These resampling techniques include the methods of oversampling of the rare events, undersampling of the common events in the data, and synthetic minority over-sampling technique (SMOTE). The predictive models of decision trees, logistic regression and rule induction are applied with SAS Enterprise Miner (EM) software to the revised data. The studied data set is of home mortgage applications which includes a target variable with an occurrence rate of the rare event being 0.8%. The authors varied the percentage of the rare event from the original of 0.8% up to 50% and monitored the associated performances of the three predictive models to see which one worked the best.

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

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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