Personalized workload management in badminton using a machine learning model

The classification model in machine learning has been employed to address different problems. Machine Learning classification is an effective method to realize customer churn prediction. This article provides a comparative study of machine learning from the perspective of predicting customer churn. Customer churn is one of the obstacles hindering the development of companies. Through classification approaches based on machine learning, customer churn can be predicted precisely, thus providing decision-making capabilities to these companies. Customer churn prediction is a typical classification problem and can be addressed by the use of a decision tree or random forest model. In this paper, decision tree and random forest models are employed to predict customer churn, using sales data provided by a chemical company from 2012 to 2020. We analyze the underlying risk of customer churn and the customer churn factors, including the low-priced count (LC), total amount of money (TM), and creation time (CT). The prediction results of the two models are evaluated by calculating various metrics. The experimental results indicate that the low-priced count (LC) is the most essential factor for customer churn. The results of the comparison, considering the training error and generalization error including: confusion matrix, ROC curve, AUC, precision, recall, and F1 score, reveal that the random forest model has better prediction accuracy than the decision tree model.

Teicoplanin 24-h loading dose regimen using a decision tree model to target serum trough concentration of 15–30 μg/mL: A retrospective study

To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP) and compare the classification accuracy of the new DT models to that achieved by previously published cut points for youth with CP. Youth with CP (GMFCS levels I-III) (N = 51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (<1.5 METs), light-intensity PA (LPA) (≥1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (≥3 METs). Models were trained and cross-validated using the "rpart" and "caret" packages within R. For the VA (VA_DT) and VM DT (VM_DT), a single threshold differentiated LPA from SED, whereas the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracies for the VC_DT were 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III. The corresponding cross-validation accuracies for the VM_DT were 80.5%, 75.6%, and 84.2%. Within each GMFCS level, the DT models achieved better PA intensity recognition than previously published cut points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut points misclassified 40% of the MVPA activity trials. The GMFCS-specific cut points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.

Regional landslide susceptibility prediction (LSP) research is of great significance to the prevention and control of landslides. This study focuses on the LSP modelling based on the decision tree model. Taking the northern part of An’yuan County of Jiangxi Province as an example, 14 environmental factors including elevation, gully density and lithology are obtained based on geographical information system (GIS) and remote sensing satellite. Frequency Ratio method and C5.0 decision tree (DT) model are coupled to build DT model for LSP modelling. Then the predicted results are graded into five attribute intervals. Finally, LSP performance of DT model is evaluated by comparing the area value under the receiver operating characteristic curve (ROC) and classification of landslide susceptibility. The results show that the AUC accuracy of the C5.0 DT model is 0.805, and the LSP results of the C5.0 DT model are consistent with the actual distribution pattern of landslides in this County.

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm to automatically detect free text radiology reports that have a follow-up recommendation using natural language processing (NLP) techniques and machine learning models. The data set used in this study consists of 6000 free text reports from the author's institution. NLP techniques are used to engineer 1500 features, which include the most informative unigrams, bigrams, and trigrams in the training corpus after performing tokenization and Porter stemming. On this data set, we train naive Bayes, decision tree, and maximum entropy models. The decision tree model, with an F1 score of 0.458 and accuracy of 0.862, outperforms both the naive Bayes (F1 score of 0.381) and maximum entropy (F1 score of 0.387) models. The models were analyzed to determine predictive features, with term frequency of n-grams such as "renal neoplasm" and "evalu with enhanc" being most predictive of a follow-up recommendation. Key to maximizing performance was feature engineering that extracts predictive information and appropriate selection of machine learning algorithms based on the feature set.

Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites.

To develop a logistic regression model, artificial neural network (ANN) model and decision tree (DT) model for the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) to compare the performance of the three models. A total of 425 patients with MCI were screened from the original cohort. The actual follow up included 361 patients, with AD as the outcome variable. Three kinds of prediction models were developed: a logistic regression model, ANN model and DT model. The performance of all three models was measured with accuracy, sensitivity, positive predictive value and area under the receiver operating characteristic curve. A total of 121 patients with MCI developed AD, and the average conversion rate was 9.49% per year. The ANN model had higher accuracy (89.52 ± 0.36%), area under the receiver operating characteristic curve (92.08 ± 0.12), sensitivity (82.11 ± 0.42%) and positive predictive value (75.26 ± 0.86%) than the other two models. The first five important predictors of the ANN model were, in order, ADL score, age, urine AD-associated neuronal thread protein, alcohol consumption and smoking. For the DT model, they were age, activities of daily living score, family history of dementia, urine AD-associated neuronal thread protein and alcohol consumption. For the logistic regression model, they were age, sex, activities of daily living score, alcohol consumption and smoking. The logistic regression, ANN and DT models performed well at predicting the transition from MCI to AD with ideal stability. However, the ANN model had the best predictive value. Increased age, activities of daily living score, urine AD-associated neuronal thread protein, alcohol consumption, smoking and sex were important factors. Geriatr Gerontol Int 2021; 21: 43-47.

ObjectiveThis study investigates the factors influencing sedentary behavior in older adult Chinese stroke patients using decision trees and logistic regression models.MethodsConvenience sampling method was employed to enroll 346 respondents aged ≥60 years with stroke from the Department of Neurology of three tertiary-level A hospitals in Heilongjiang province, based on the inclusion criteria. The Sedentary Behavior Questionnaire for Older Adults, the International Physical Activity Questionnaire Short Form (IPAQ-S), the Pittsburgh Sleep Quality Index (PSQI), the Self-Rating Depression Scale (SDS), and the Social Support Scale (SSRS) were used to assess sedentary behavior, physical activity level, sleep quality, depressive symptoms, and social support, respectively. Decision tree and logistic regression models were employed to analyze the factors related to sedentary behavior in older adult stroke patients.ResultsOf the 346 respondents, 233 (67.3%) had sedentary behavior. The logistic regression model showed that education level (OR = 2.843, 95%CI: 1.219–6.626), BMI (OR = 3.686, 95%CI: 1.838–7.393), longest consecutive sitting time (OR = 3.853, 95%CI: 1.867–7.953), and sleep quality (OR = 3.832, 95%CI: 1.716–8.557) were identified as risk factors for sedentary behavior in older adult stroke patients, while drink alcohol (OR = 0.386, 95%CI: 0.184–0.809) and physical activity level (OR = 0.064, 95%CI: 0.030–0.140) were identified as protective factors for sedentary behavior. Besides, the decision tree model showed that physical activity level, longest consecutive sitting time, sleep quality, BMI, depressive symptoms, and age were associated with sedentary behavior. The sensitivity and specificity of the logistic regression model were 69.9 and 93.1%, respectively, and the area under the receiver operating characteristic (ROC) curve was 0.900 (95% CI: 0.863–0.938). The sensitivity and specificity of the decision tree model were 66.4, and 93.1% respectively, and the area under the ROC curve was 0.860 (95% CI: 0.816–0.904).ConclusionOur findings indicated that physical activity level, longest consecutive sitting time, sleep quality, and BMI were key factors associated with sedentary behavior. To achieve the purpose of improving rehabilitation effect and quality of life, this study combining decision trees with logistic regression models was of high value in studying factors influencing sedentary behavior in older adult stroke patients.

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.

Statistical models are essential to understand the occurrence and spread of microbes to support decision-making in microbiology and epidemiology. Antimicrobial resistance (AMR) is a multifaceted global problem and a significant threat to sustainable modern healthcare. This thesis aims to identify knowledge gaps in the AMR research field and explain the added value of using statistical models and novel spatiotemporal data to predict and identify risk factors for the occurrence and spread of AMR. Strategic action plans to tackle the increasing international threat of AMR are based upon research agendas that are informed using knowledge gaps in the AMR research field. Currently, these knowledge gaps are identified manually and are often subjective. Chapter 2 describes how bibliometric data-driven methodology can be used to identify knowledge gaps in AMR research. To this end, twenty years of AMR related articles were extracted using the PubMed search engine. With structural topic modelling I identified the topics comprising the AMR research field, while topic clusters were created using hierarchical clustering on the topic proportions. Potential AMR knowledge gaps were obtained using Spearman’s correlation between topic clusters and topics and between individual topics. A total of 88 topics and seven topic clusters were identified from 158 616 scientific AMR research articles. In total, 421 potential knowledge gaps were identified between the topic clusters and topics and 2 663 between individual topics. Key knowledge gaps between molecular and laboratory AMR research were highlighted. The knowledge gaps between AMR research regarding water and the environment and both institutional and international surveillance topics were highlighted at the topic level. These results provide an innovative, data-driven way to identify knowledge gaps in AMR research. Surgical site infections (SSI) make up 19.6% of healthcare-associated infections (HAIs) in Europe [98]. Risk factor identification studies for the occurrence of SSI do not usually specify how continuous variables cut-offs are determined. In most cases, they use standard medical cut-offs without considering the data being studied. Chapter 3 identifies the risk factors for the occurrence of SSI for digestive, thoracic and orthopaedic system surgeries using standard medical and data-driven cut-off values. Retrospective surgical procedure data, individual electronic health records, pharmaceutical data and laboratory data were used from the Erasmus MC University Medical Centre in The Netherlands. Risk factors for the occurrence of SSI were identified using a multivariate forward-step logistic regression model. Standard medical cut-off values were compared with cut-offs determined from the data. For digestive, orthopaedic and thoracic system surgical procedures, the risk factors identified for the occurrence of SSI were preoperative temperature of 38 oC and antibiotics used at the time of surgery. C-reactive protein (CRP) and the duration of the surgery were identified as risk factors for digestive surgical procedures. Being an adult (age ≥ 18) was identified as a protective effect for thoracic surgical procedures. Data-driven cut-off values identified for temperature, age, and CRP, explained the occurrence of SSI outcome up to 19.5% better than standard medical cut-off values. Future studies should investigate if data-driven cut-offs can add value to explain the clinical outcome being modelled and not solely rely on standard medical cut-off values for continuous variables to identify risk factors. Transmission of harmful microorganisms (HMO) poses a major threat to patients and healthcare workers in healthcare settings. The most effective countermeasure against these transmissions is the adherence to hand hygiene policies, but adherence rates are relatively low and vary over space and time. The spatiotemporal effects of varying levels hand hygiene compliance on the transmission and spread of hand-transmitted HMO in a closed healthcare setting must still be quantified. Chapter 4 describes how identifies healthcare worker occupation group of potential super-spreaders and the spatiotemporal effects on the hand transmission of HMO quantified for varying levels of hand hygiene compliance (HHC) caused by this group using their spatiotemporal movements. Spatiotemporal data were collected in the University Medical Center Groningen (UMCG) using radio frequency identification technology. The effects of five probability distributions of HHC and three harmful microorganism transmission rates were simulated using a dynamic agent-based simulation model. The effects of initial simulation assumptions on the simulation results were quantified using five risk outcomes. Nurses were identified as the potential super-spreader healthcare worker occupation group. During lack of HHC (5%) and high transmission rates (5% per contact moment), a colonised nurse can transfer microbes to three of the 17 healthcare worker or patients encountered during the 98.4 minutes of visiting 23 rooms while colonised. The HMO transmission potential for nurses is higher during weeknights (5 pm – 7 am) and weekends as compared to weekdays (7 am – 5 pm). Spatiotemporal behaviour and social mixing patterns of healthcare can change the expected number of hand transmissions and spread HMO by super-spreaders in a closed healthcare setting. These insights can be used to evaluate spatiotemporal safety behaviours and develop infection prevention and control strategies. Vancomycin-resistant enterococci (VRE) is can cause severe patient health and monetary burdens. The odds of a hospital patient acquiring VRE increases when using antibiotics and when prior room occupants had VRE, but the antibiotic use of prior room occupants are often neglected. Chapter 5 describes how the occurrence and spread of VRE can be explained using intrahospital patient movements (IPM) and their antibiotic use between hospital wards. Retrospective IPM, antibiotic use and PCR screening data were used from a hospital in the Netherlands. A dynamic directed spatiotemporal graph was developed, and together with the PageRank algorithm used to calculate two daily centrality measures to summarise the flow of patients and antibiotics at the ward level. With a decision tree and random forest model I predicted the daily occurrence of VRE for every ward and compared the models’ performance using a 30% test sample. The decision tree model produced a simple set of rules that can be used to determine the daily probability of VRE occurrence for each hospital ward. The decision tree model achieved an area under the curve of 0.685 and the random forest model 0.886 on the test set. These results confirm that the random forest model performs better than a single decision tree for all levels of model sensitivity and specificity at the cost of model simplicity. An early warning system for VRE can be developed and inform infection prevention plans and outbreak strategies further using these results. In summary, this thesis showed that data-driven statistical models can improve our understanding of antimicrobial resistance. It considers how different sources of spatiotemporal data may be used to predict its occurrence and spread of AMR in hospitals.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Effective classification of patient therapy is essential for enhancing healthcare delivery and optimizing resource allocation. This paper introduces an automated methodology using decision tree (DT) models to categorize patients as either inpatient or outpatient, utilizing clinical and demographic features. The proposed model exhibited exceptional prediction performance, with an accuracy of 99.22%, signifying dependable and resilient classification capacity. The analysis of feature significance indicated that haemoglobin, haematocrit, and erythrocyte levels were the most critical determinants affecting therapy classification. The DT model utilizes structured healthcare data to assist physicians in making evidence-based choices, minimizing human mistakes, and enhancing patient management. Health care providers can understand the rationale behind each categorisation because to the DT's interpretability, which makes the decision-making process transparent. This automated system provides a scalable solution for hospitals and clinics aiming to optimize treatment allocation, improve operational efficiency, and provide personalized patient care. The study illustrates that the amalgamation of healthcare data analytics with machine learning (ML) approaches may markedly improve clinical procedures and patient outcomes.

Exploring sex classification from earprints - A comparison of supervised machine learning algorithms and conventional linear discriminant analysis.

&lt;abstract&gt;&lt;p&gt;In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.&lt;/p&gt;&lt;/abstract&gt;

Several publications concerning decision tree (DT) analysis in medical fields have recently demonstrated its usefulness for defining prognostic factors in various diseases. However, there are minimal reports on the predictors of adverse drug reactions. We attempted to use DT analysis to discover combinations of multiple risk factors that would increase the risk of nephrotoxicity associated with vancomycin (VCM). To demonstrate the usefulness of DT analysis, we compared its predictive performance with that of multiple logistic regression analysis. A single-centre, retrospective study was conducted at Hokkaido University Hospital. A total of 592 patients, who received intravenous administrations of VCM between November 2011 and April 2016, were enrolled. Nephrotoxicity was defined as an increase in serum creatinine of ≥0.5mg/dL or a ≥50% increase in serum creatinine from the baseline. Risk factors for VCM nephrotoxicity were extracted from previous reports. In the DT analysis, a chi-squared automatic interaction detection algorithm was constructed. For evaluating the established algorithms, a 10-fold cross validation method was adopted to calculate the misclassification risk of the model. Moreover, to compare the accuracy of the DT analysis, multiple logistic regression analysis was conducted. Eighty-seven (14.7%) patients developed nephrotoxicity. A VCM trough concentration of ≥15.0mg/L, concomitant medication (vasopressor drugs and furosemide), and a duration of therapy ≥14days were extracted to build the DT model, in which the patients were divided into 6 subgroups based on variable rates of nephrotoxicity, ranging from 4.6 to 69.6%. The predictive accuracies of the DT and logistic regression models were similar (87.3%, respectively), indicating that they were accurate. This study suggests the usefulness of DT models for the evaluation of adverse drug reactions.