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.

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.

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

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.

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

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

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.

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

For telecom companies, customer attrition is a major problem that has a direct impact on retention and revenue. In order to predict churn and take quick retention actions based on customer history, a deep learning model built on Keras and TensorFlow is used for this project. Deep learning enhances the ability to identify intricate data associations when compared to more conventional techniques like logistic regression and decision trees. Data collection, preprocessing, training, and model evaluation are all part of the project. Tenure, charges, and demographics of customers are included in a publicly available data set. Data preprocessing includes feature normalization, categorical variable encoding, and missing value handling. Accuracy and loss criteria are used to train and assess a neural network model with hidden layers. When it comes to identifying risky customers, the deep learning model outperforms the conventional methods due to its high accuracy. The method shows how deep learning can be used in predictive analytics for client retention.

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.

Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees

Sports injuries are a significant concern for athletes, often leading to performance decline, financial losses, and long-term health issues. With the advancement of artificial intelligence (AI), data-driven models have emerged as effective tools to predict injury risk and support preventive strategies. This study investigates the application of three AI approaches-Logistic Regression, Decision Trees, and Deep Learning-for athlete injury prediction. Logistic Regression is employed as a baseline statistical model to identify linear risk factors, while Decision Trees capture non-linear relationships and provide interpretable decision rules. Deep Learning techniques, particularly neural networks, are applied to handle complex, high-dimensional data, such as biometric, physiological, and training load variables. Model performance is evaluated using standard metrics including accuracy, precision, recall, and F1-score. The comparative analysis highlights the strengths and limitations of each approach, emphasizing tradeoffs between interpretability and predictive power. Findings suggest that while Logistic Regression and Decision Trees are valuable for transparent and explainable predictions, Deep Learning achieves superior accuracy in capturing hidden patterns within athlete datasets. This research contributes to the development of intelligent sports science frameworks, enabling coaches, medical professionals, and athletes to implement data-driven strategies for injury prevention and performance optimization. This study presents a comparative analysis of three AI-based techniques-Logistic Regression, Decision Trees, and Deep Learning-for predicting injuries in athletes. The obtained outcomes indicate how successful the two suggested models are in letting the transfer learning model achieve Linear regression accuracy of 93.15% and RNN accuracy of 91.24%.

The purpose of this methodological study was to compare methods of developing predictive rules that are parsimonious and clinically interpretable from electronic health record (EHR) home visit data, contrasting logistic regression with three data mining classification models. We address three problems commonly encountered in EHRs: the value of including clinically important variables with little variance, handling imbalanced datasets, and ease of interpretation of the resulting predictive models. Logistic regression and three classification models using Ripper, decision trees, and Support Vector Machines were applied to a case study for one outcome of improvement in oral medication management. Predictive rules for logistic regression, Ripper, and decision trees are reported and results compared using F-measures for data mining models and area under the receiver-operating characteristic curve for all models. The rules generated by the three classification models provide potentially novel insights into mining EHRs beyond those provided by standard logistic regression, and suggest steps for further study.

In today’s world, the most widely means of payment in online and offline is the credit card [cc] which provides a method to provide cashless shopping. Credit cards being used are escalating and the fraudulent activity is also increasing due to which a huge amount of money is lost. Thus, it is very much essential to have mechanisms to overcome fraudulent activities. The existing systems used the users spending criteria to find out whether a fraud is happening. Even when multiple algorithms were used to predict fraudulent activity, the usage of a specific attribute led to the failure of few systems. The proposed system detects fradulent activity of cc using ML. When a user makes a transaction, a combination of standard algorithms like logistic regression and decision trees along with hybrid algorithms like majority voting method and Adaboost are used to stop fraudulent transactions. The results of the hybrid algorithms form clusters by using the k-means algorithm and finally the Naive Bayes is used to do the final prediction whether it is fraud or not. Based on the resultsk it can be analyzed that the prediction is not accurate when the trained dataset is small and the accuracy increases with increase in the dataset.

CKD (Chronic Kidney Diseases) is a persistent medical state categorized by the kidney damage that hinders their ability to effectively filter blood. Over time, this progressive disease can result in kidney failure. This project compares the performance of the Support Vectos Machines (SVM), logistic regression and Decision Tree algorithms for predicting the risk of CKD. In this project, the dataset utilized comprises a total of 25 attributes, consisting of 11 numerical features and 14 nominal features. In the training of machine learning algorithms for prediction, all 400 instances from the dataset are utilized. Among these instances, 250 are labeled as CKD cases, indicating the presence of chronic kidney disease, while the remaining 150 instances are categorized as non-CKD cases, denoting the absence of the condition. We utilized the UCI dataset, which underwent preprocessing to handle missing data. Using Python, we trained and built Support Vectors Machines (SVM), Logistic Regression, and Decision Tree models. The accuracy achieved with SVM was 97.3%, Logistic Regression was 93.8%, and Decision Tree yielded 95%, which are notable results.