Classifying Composition of Software Development Team Using Machine Learning Techniques

A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees

ObjectiveThis study aimed to systematically identify risk factors for urinary catheter-related hematuria in patients with acute myocardial infarction (AMI). By integrating logistic regression and decision tree models, we sought to develop actionable strategies for risk stratification and complication prevention.MethodsA retrospective analysis of 209 AMI patients was conducted to evaluate predictors of hematuria, including demographics, coagulation indices (INR, platelets), and procedural variables. Logistic regression and decision tree (CART algorithm) models were employed to identify risk factors and their interactions. Model performance was assessed using ROC-AUC, sensitivity, and specificity.ResultsThe incidence of catheter-related hematuria was 32.5%. Both models identified persistent agitation during catheter indwelling and PLT ≤ 246 as common predictors. The logistic regression model specifically identified Gender (OR = 0.202), patient awareness of catheter purpose and precautions (OR = 0.470), and emergency catheter placement (OR = 2.257) as significant factors. The decision tree model uniquely identified INR > 0.955 and repeated complaints of urethral pain as predictors.ConclusionHematuria in AMI patients results from coagulation dysfunction, procedural trauma, and behavioral factors. The combined use of logistic regression and decision trees enhances risk stratification. Clinical strategies should prioritize gentle catheterization, dynamic coagulation monitoring, and patient education to reduce complications.

Context: It is known that men practitioners are predominant in software development teams over women. In recent years, researchers have investigated gender inequality from different perspectives, such as gender bias and suggestions to increase diversity in development teams. However, while most of the existing literature investigates the perception of women practitioners, the vast majority of the software development workforce – men – has received less attention. Goal: This work investigates men’s perceptions of gender inequality in software development teams and their experiences compared in software development to women. Method: Our study conducted a survey with 217 male practitioners, using a questionnaire consisting of 27 questions. The collected data was analyzed using a combination of quantitative and qualitative techniques, including graphical representations, percentages, and the grounded theory methodology. Results: Our findings reveal that most men do not perceive any sexist behavior among their team members and they are satisfied with the performance of their functions. In addition, their main suggestions to increase their participation in software development projects are offering training courses and improving the interaction among team members. Conclusions: The barriers and challenges that men practitioners face in software development teams are not the same as those faced by women. In addition, they consider that women do not work in software development due to a lack of affinity and knowledge about coding.

Environmental Factors Prediction in Preterm Birth Using Comparison between Logistic Regression and Decision Tree Methods:  An Exploratory Analysis

The growth and development of children under five (toddlers) affect their health conditions. Each region uniquely identifies the main factors influencing the toddler's health condition. The status of toddlers is generally categorized into two classes, namely normal and abnormal, so it is often found that the condition of toddler status is in the form of multi-response variables. Combining the two binary classes' response variables will form a multiclass response variable requiring different model development techniques and performance measurements. This study aims to determine the main factors that affect toddlers' health conditions in Malang, Indonesia, build multiclass logistic regression and decision tree classification models, and measure the model's performance. The Chi-square test selected predictor features as the input of multiclass logistic regression and decision tree models. From the feature selection, four main factors influence the status of toddlers' health conditions in Malang: the mother's history of diabetes before pregnancy, the father's blood pressure, psychological condition, and drinking water quality. The decision tree model performs better than the logistic regression model on the various performance measures used.

In recent years, the prevalence of hand-foot-mouth disease (HFMD) in China and some other countries has caused worldwide concern. Mild cases tend to recover within a week, while severe cases may progress rapidly and tend to have bad outcome. Since there is no vaccine for HFMD and anti-inflammatory treatment is not ideal. In this study, we aimed to establish a valid forecasting model for severe HFMD using common laboratory parameters. Retrospectively, 77 severe HFMD cases from Zhengzhou Children's hospital in the peaking period between years 2013 to 2015 were collected, with 77 mild HFMD cases in the same area. The study recorded common laboratory parameters to assist in establishment of the severe HFMD model. After screening the important variables using Mann-Whitney U test, the study also matched the logistic regression (LR), discriminant analysis (DA), and decision tree (DT) to make a comparison. Compared with that of the mild group, serum levels of WBC, PLT, PCT, MCV, MCH, LCR, SCR, LCC, GLO, CK-MB, K, S100, and B in the severe group were higher (p < 0.05), while MCR, EOR, BASOR, SCC, MCC, EO, BASO, NA, CL, T, Th, and Th/Ts were lower (p < 0.05). Five indicators including MCR, LCC, Th, CK-MB, and CL were screened out by LR and the same for DA, and five variables including EO, LCC, CL, GLO, and MCC screened out by DT. The area under the curve (AUC) of LR, DA, and DT was 0.805, 0.779 and 0.864, respectively. The findings were that common laboratory indexes were effectively used to distinguish the mild HFMD cases and severe HFMD cases by LR, DA, and DT, and DT had the best classification effect with an AUC of 0.864.

A recent survey indicates that 53 % of software development projects are over budget, behind schedule, or deliver fewer features than originally specified. Statistics also show that 31 % of development projects end up being cancelled or terminated prematurely. Among those completed projects, only 61 % of them satisfy originally specified features and functions. In today's environment, one of the greatest challenges a project manager constantly face with is to keep the projects under control in terms of budget and development time frame. A successful software development project relies on many factors; it is not that easy to control all of them and continually keeping those entire factors all going well together. The goal of this paper is to introduce a mathematical model and prove that a software development team can rely on it to accurately predict, calculate the risks and their impacts on the success of the project. Our objective is to conceptualize the model into a scientific tool that can be used to understand and calculate the risks of a development project. Subsequently, the software development team can take appropriate actions to mitigate the risks, and as a result, the project manager have a better control of the budget and development time frame of the project. It is the author's believe that if we can identify and control problems at early stages, we can significantly increase the chance of success of the development project. The model and the software tool written by the author in this paper to calculate the risks and weight their impacts on a project can be used to identify problems and their potentials risk at early stage. The tool also allows a project manager to apply the model and obtain results without getting involved in too many mathematical calculations. Although the model introduced in this paper can generally provide an accurate picture of what, how, and when things may go wrong at the beginning of a typical software development project, there are areas need further fine tuned, especially when it is used for a particular industry or at later stages of software development cycle.

A form of cancer known as lung cancer usually appears in the cells that line the lungs' air passages. It is the primary cause of cancer-related deaths in both genders. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are two separate types of non-small cell lung cancer (NSCLC). Recognition of the differentially expressed genes (DEGs) is necessary to distinguish between healthy and diseased states molecularly. Biomedical books and microarray investigations are two common places to find details concerning gene expression variations. A gene is said to be highly expressed if there is a significant statistical distinction or change in reading counts or expression level indices between two different experimental conditions. A good gene expression strategy leads to a good gene silencing strategy, which subsequently slows or stops the cancer progression. In this paper, we present a robust analysis of the mRNA data from the LUAD data to find the most differentially expressed genes. Then we employ several machine learning techniques to categorize the samples into the five stages of lung cancer. We investigated four distinct models; including linear regression, logistic regression, RANSAC regression, and decision trees. The models achieved 100%, 91%, 98%, and 100% accuracies for 12 differential expression genes using linear regression, logistic regression, RANSAC regression, and decision trees respectively. The logistic regression and decision trees achieved 100% and 98% accuracies for 5665 differential expression genes. The proposed models achieved the highest accuracies in comparison with the state-of-the-art models.KeywordsGene expressionDEGsLUADLung cancerClassificationMachine learningDecision treeRANSACLogistic regressionLinear regression

The purpose of this mixed-methods study is to explore the effectiveness of Agile methods in addressing inefficiencies within software development projects, related issues, delays, excessive defects, and concerns with improved collaboration among the teams. This paper seeks to measure the value that Agile brings to organizations and recommend the implementation of these methods as a way of addressing the challenges that organizations experience when using conventional systems in software development. No changes to the basic Agile concept were made here and thus the research is based on methods that are already implemented such as Scrum and Kanban. Measures of productivity including project completion time, number of defects, and satisfaction level of the software development teams were obtained over six months from three companies. This research is innovative in the sense that it uses both the quantitative and qualitative methods in conducting the assessment to determine whether or not the Agile methodologies improve software development effectiveness and team performance in contrast to the Waterfall model. The results of this study indicate that Agile techniques enhanced productivity combined with the quality of team relations and can be used for further expansion in the sphere of software engineering. Therefore, it is recommended that Agile practices be implemented as a way of fixing problems that are standard in software development.

Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

Since the turn of the 20th century many leadership theories have emerged and evolved over time. Transformational leadership is one of the older, more prominent theories and has been used in organizations and in political arenas for many years. Complexity leadership theory, on the other hand, is relatively new in practice and academia. To date, there is very little literature addressing the similarities and differences between the transformational and complexity leadership theories as well as their comparative value in organizational contexts. Both the Complexity Leadership and Transformational Leadership theories are presented and the similarities and differences of each theory are discussed and evaluated. The purpose of this comparison and evaluation is present the theory’s foundations so that they may be applied to the challenges presented by IT software development teams. The application will result in determining and recommending the more effective leadership style for IT-based teams as well as presenting tips for successful IT software development projects. Understanding different leadership styles and their response to team challenges can ultimately contribute to the leader’s ability to effectively manage IT teams and successfully leading IT software development projects in organizations.

Managers recognize that software development project teams need to be developed and guided.Although technical skills are necessary, non-technical (NT) skills are equally, if not more, necessary for project success.Currently, there are no proven tools to measure the NT skills of software developers or software development teams.Behavioral markers (observable behaviors that have positive or negative impacts on individual or team performance) are beginning to be successfully used by airline and medical industries to measure NT skill performance.The purpose of this research is to develop and validate the behavior marker system tool that can be used by different managers or coaches to measure the NT skills of software development individuals and teams.This paper presents an empirical study conducted at the Software Factory where users of the behavior marker tool rated video clips of software development teams.The initial results show that the behavior marker tool can be reliably used with minimal training.

To identify correlates of the incidence of cognitive impairment among older Chinese populations through the use of logistic regression analysis-based decision tree approaches. Correlates of cognitive impairment among older Chinese adults were identified through logistic regression analyses, with significant variables subsequently being incorporated into a decision tree analysis, with the CHAID method being employed for pre-pruning. The risk score derived from the combination of logistic regression and decision tree analyses (0.237) was lower than that derived from a decision tree analysis alone (0.389). The primary factors related cognitive impairment in this patient population included age, gender, residence status, physical health status, and caring for grandchildren. A combination of logistic regression and decision tree analyses can lower predicted risk scores, enabling the subdivision of populations with different characteristics and providing intuitive and specific insight regarding the effects of individual variables on predictive analyses. Overall, these results suggest that older adults in rural areas of China should be the focus of further cognitive impairment screening and interventions, particularly for older women.

Learning From Highly Effective Teams: What Can We Apply to the Gastrointestinal Endoscopy Unit Team?

Purpose This study aims to develop and validate a machine learning-based risk prediction model for 1-year cardiac mortality in acute myocardial infarction (AMI) patients, utilizing a multidimensional dataset that integrates traditional risk factors, environmental determinants, and angiographic findings. Methods Data was sourced from a multicenter prospective cohort study (NCT 03297164) along with electronic medical records from a large tertiary hospital, covering the period from January 1, 2017, to July 28, 2022. The patient population from the tertiary hospital constituted the derivation cohort, while patients from other participating centers served as the external validation cohort. Six machine learning algorithms — logistic regression, XGBoost, LightGBM (LGBM), Gradient Boosting, random forest, and decision tree — were applied, with feature selection performed using the SelectFromModel method. The best-performing model underwent both internal and external validation, with its predictive performance compared to the GRACE score. Result The derivation cohort was divided into training and testing cohorts. In the training cohort, six machine learning algorithms were employed to screen variables and construct models using five-fold cross-validation (Figure 2A). All models achieved an area under the receiver operating characteristic curve (AUC) &amp;gt; 0.8 (Figure 2B). Delong's test revealed no significant differences in AUC among the algorithms. Considering other evaluation metrics and brier score, the LGBM (AUC 0.85) model was selected, incorporating the following variables: age, body mass index, smoking history, temperature, PM2.5, Gensini score, weighted diffuse lesion value, left ventricular end-diastolic diameter, ejection fraction, serum creatinine, cardiac troponin I, glucose, low-density lipoprotein cholesterol, B-type natriuretic peptide, and D-dimmer. External validation of the LGBM model yielded an AUC of 0.80, while the GRACE score achieved AUCs of 0.73 and 0.74 in internal testing and external validation, respectively (Figure 2C). Predicted LGBM scores in the training cohort were stratified into ten groups by decile. Patients were categorized into low-, intermediate-, and high-risk groups based on the actual incidence of cardiac death (Figure 2D). Using these cut-off values, risk stratification was applied to both the internal testing cohort and the external validation cohort, and the actual event rates were calculated (Figure E). Logistic and Cox regression analyses demonstrated significant differences between risk groups (P &amp;lt; 0.001, Figures F-I), with the LGBM-based stratification outperforming that based on the GRACE score. Conclusion Based on recent large-scale multicenter cohort data and incorporating environmental factors along with angiographic findings, this study developed and validated a novel prediction model for 1-year cardiac death in AMI patients, which demonstrated superior performance compared to the GRACE score.Flow chart Main Findings