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

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.

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.

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 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 the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before. The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies. In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively. In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality. In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.

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.

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.

Cognitive science is a technology which focuses on analyzing the human brain using the application of DM. The databases are utilized to gather and store the large volume of data. The authenticated information is extracted using measures. This research work is based on detecting the sarcasm from the text data. This research work introduces a scheme to detect sarcasm based on PCA algorithm, K-means algorithm, and ensemble classification. The four ensemble classifiers are designed with the objective of detecting the sarcasm. The first ensemble classification algorithm (SKD) is the combination of SVM, KNN, and decision tree. In the second ensemble classifier (SLD), SVM, logistic regression, and decision tree classifiers are combined for the sarcasm detection. In the third ensemble model (MLD), MLP, logistic regression, and decision tree are combined, and the last one (SLM) is the combination of MLP, logistic regression, and SVM. The proposed model is implemented in Python and tested on five datasets of different sizes. The performance of the models is tested with regard to various metrics.

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p&lt;0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p&gt;0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

The use of decision trees and artificial neural networks (ANNs) in health-care research is widespread, as they enable health-care providers with the tools they need to make better medical decisions with their patients. ANNs specifically are extremely helpful in predictive research as they can provide investigators with knowledge about future trends and patterns. However, a major downside to ANNs is their lack of interpretability. Understandability of the model is important as it ensures the outcomes are true to the dataset’s original labels and are not impacted by algorithmic bias. In comparison, decision trees map out their entire process before providing the results, which leads to a higher level of trust in the model and the conclusions it supplies the investigators with. This is essential as many historical datasets lack equal and fair representation of all races and sexes, which might directly correlate to a lesser treatment given to females and individuals in minority groups. Here, we review existing work around the differences and connections between ANNs and decision trees with implications for research in health care.

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

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 case history of an exploratory well in Lower Indus basin in Pakistan, is described where Massive Sand Deep, Massive Sand Shallow and Basal Sands of Cretaceous Lower Goru formation were target reservoirs, however, during drilling, positive indications were also observed in Sembar formation of early Cretaceous age. Successful formation evaluation of three target reservoirs posed a great challenge, due to bad hole conditions and bad cement job. This resulted in risk of no formation zonal isolation between target reservoirs along with risk of operational issues due to high mud weight. The strategy was adopted to test and conclude each reservoir with marginal potential, as such, other reservoir formation evaluations were not compromised. Decision tree based methodology was extensively used to convey the plan, which really proved helpful and avoided confusions for all shareholders and decision makers. A thorough critical analysis of previous formation testing/Drill Stem Testing (DST) jobs and their success rates were performed. Analysis of cement logs, mud and wireline logs were performed to select best evaluation jobs for respective targets. Similarly for formation testing jobs, zones of interest needed further optimization considering zonal isolation and presence of cement behind pipe. The testing strategy resulted in the best possible formation evaluation and finally conclusion of all three targets reservoirs. With this, cost optimization was achieved as all targets were tested in single well. Use of decision tree was successful in conveying status and way forward at each step, which avoided confusion in all shareholders. Also, as future way forward was already put in place in form of decision tree, based on encouraging/ disappointing results, everyone involved knew about the course of plan. Integration of all available technical data was very critical which helped maneuvering in taking decisions. The strategy became so successful, that shareholders advised to make it part of every new project. This project presented and prompted company and joint venture partners, the valuable use of flow charts and decision trees and proved to be an excellent tool to convey information, to stay aligned and to be aware of future strategy.