Understanding parental preference for childcare: a cross-sectional study in Chongqing, China.

This study analyzed the factors influencing childcare preference and the relationship between childcare preference and childcare service demand scale, using data collected from a questionnaire survey of 3,921 parents of infants and toddlers in Chongqing, China. The results indicate that parents with higher incomes, higher education levels, older ages, multiple infants, and dual-career living in urban areas have a stronger preference for childcare. In the shared or grandparent care model, the childcare preference is not obvious. Parents of infants tend to choose childcare institutions that provide reception services, early education, and convenience services. Higher quality environmental facilities tend to reduce the preference of parents for childcare.

Left atrial function in patients with rheumatic mitral stenosis: addressing prognostic insights beyond atrial fibrillation prediction.

Aims: Rheumatic mitral stenosis (MS) frequently leads to impaired left atrial (LA) function because of pressure overload, highlighting the underlying atrial pathology. Two-dimensional speckle tracking echocardiography (2D-STE) offers early detection of LA dysfunction, potentially improving risk assessment in patients with MS. This study aims to evaluate the predictive value of LA function assessed by 2D-STE for clinical outcomes in patients with MS. Methods and results: Between 2011 and 2021, patients with MS underwent LA function assessment using 2D-STE, with focus on the reservoir phase (LASr). Atrial fibrillation (AF) development constituted the primary outcome, with death or valve replacement as the secondary outcome. Conditional inference trees were employed for analysis, validated through sample splitting. The study included 493 patients with MS (mean valve area 1.1 ± 0.4 cm2, 84% female). At baseline, 166 patients (34%) had AF, with 62 patients (19%) developing AF during follow-up. LASr emerged as the primary predictor for new-onset AF, with a threshold of 17.9%. Over a mean 3.8-year follow-up, 125 patients (25%) underwent mitral valve replacement, and 32 patients (6.5%) died. A decision tree analysis identified key predictors such as age, LASr, severity of tricuspid regurgitation (TR), net atrioventricular compliance (C n), and early percutaneous mitral valvuloplasty, especially in patients aged ≤49 years, where LASr, with a threshold of 12.8%, significantly predicted adverse outcomes. Conclusion: LASr emerged as a significant predictor of cardiovascular events in this MS cohort, validated through a decision tree analysis. Patients were stratified into low- or high-risk categories for adverse outcomes, taking into account LASr, age, TR severity, and C n.

Fast-and-frugal decision tree for the rapid critical appraisal of systematic reviews.

Conducting high-quality overviews of reviews (OoR) is time-consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well-established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15-32 min per application. To save time, we developed two fast-and-frugal decision trees (FFTs) for assessing the methodological quality of SR for OoR either during the full-text screening stage (Screening FFT) or to the resulting pool of SRs (Rapid Appraisal FFT). To build a data set for developing the FFT, we identified published AMSTAR 2 appraisals. Overall confidence ratings of the AMSTAR 2 were used as a criterion and the 16 items as cues. One thousand five hundred and nineteen appraisals were obtained from 24 publications and divided into training and test data sets. The resulting Screening FFT consists of three items and correctly identifies all non-critically low-quality SRs (sensitivity of 100%), but has a positive predictive value of 59%. The three-item Rapid Appraisal FFT correctly identifies 80% of the high-quality SRs and correctly identifies 97% of the low-quality SRs, resulting in an accuracy of 95%. The FFTs require about 10% of the 16 AMSTAR 2 items. The Screening FFT may be applied during full-text screening to exclude SRs with critically low quality. The Rapid Appraisal FFT may be applied to the final SR pool to identify SR that might be of high methodological quality.

Factors Predicting Treatment Adherence in Outpatients with Cancer-Related Edema: Decision Tree Analysis.

Purpose: This study aimed to determine the combination of factors associated with continuity of care in outpatients with cancer-related edema six months after the initial visit. Patients and Methods: A total of 101 outpatients were divided into two groups: continuation (n=65) and non-continuation (n=36) groups. Details regarding age, body mass index, sex, affected extremities (upper or lower), site of edema (unilateral or bilateral), International Society of Lymphology (ISL) classification, presence of distant metastasis, and overall score on the lymphedema quality of life questionnaire (LYMQOL) were obtained before initial lymphedema care. In this study, we performed a decision tree analysis using a classification and regression tree (CART) to detect the combination of factors associated with the continuity of edema care for cancer-related edema. Results: Significant differences were observed in the site of edema (unilateral or bilateral) and distant metastasis between the two groups. In the decision tree using CART analysis, the factors selected to influence the possibility of continuation were the side of edema as the first layer, and body mass index of 23.0 and distant metastasis (with/without) as the second layer. Outpatients with unilateral edema and a body mass index higher than 23.0 were most likely to be able to continue care. In contrast, outpatients with bilateral edema and distant metastasis had greater difficulty in continuing care. Conclusion: In this study, factors that were suggested to influence the continuity of cancer-related edema care were the side with edema, body mass index higher than 23.0, and distant metastasis. This information may be helpful for developing care strategies and improving patient adherence.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.

Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy.

CAR-T cell therapy is a revolutionary new treatment for hematological malignancies, but it can also result in significant adverse effects, with cytokine release syndrome (CRS) being the most common and potentially life-threatening. The identification of biomarkers to predict the severity of CRS is crucial to ensure the safety and efficacy of CAR-T therapy. To achieve this goal, we characterized the expression profiles of seven cytokines, four conventional biochemical markers, and five hematological markers prior to and following CAR-T cell infusion. Our results revealed that IL-2, IFN-γ, IL-6, and IL-10 are the key cytokines for predicting severe CRS (sCRS). Notably, IL-2 levels rise at an earlier stage of sCRS and have the potential to serve as the most effective cytokine for promptly detecting the condition's onset. Furthermore, combining these cytokine biomarkers with hematological factors such as lymphocyte counts can further enhance their predictive performance. Finally, a predictive tree model including lymphocyte counts, IL-2, and IL-6 achieved an accuracy of 85.11% (95% CI = 0.763-0.916) for early prediction of sCRS. The model was validated in an independent cohort and achieved an accuracy of 74.47% (95% CI = 0.597-0.861). This new prediction model has the potential to become an effective tool for assessing the risk of CRS in clinical practice.

Development of a decision tree model for predicting the malignancy of localized gingival enlargements based on clinical characteristics.

The present study aimed to determine the prevalence of localized gingival enlargements (LGEs) and their clinical characteristics in a group of Thai patients, as well as utilize this information to develop a clinical diagnostic guide for predicting malignant LGEs. All LGE cases were retrospectively reviewed during a 20-year period. Clinical diagnoses, pathological diagnoses, patient demographic data, and clinical information were analyzed. The prevalence of LGEs was determined and categorized based on their nature, and concordance rates between clinical and pathological diagnoses among the groups were evaluated. Finally, a diagnostic guide was developed using clinical information through a decision tree model. Of 14,487 biopsied cases, 946 cases (6.53%) were identified as LGEs. The majority of LGEs were reactive lesions (72.62%), while a small subset was malignant tumors (7.51%). Diagnostic concordance rates were lower in malignant LGEs (54.93%) compared to non-malignant LGEs (80.69%). Size, consistency, color, duration, and patient age were identified as pivotal factors to formulate a clinical diagnostic guide for distinguishing between malignant and non-malignant LGEs. Using a decision tree model, we propose a novel diagnostic guide to assist clinicians in enhancing the accuracy of clinical differentiation between malignant and non-malignant LGEs.

The BCPM method: decoding breast cancer with machine learning.

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model's efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model's performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives.

A Dynamic Trust evaluation and update model using advance decision tree for underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) are an emerging research area that is rapidly gaining popularity. However, it has several challenges, including security, node mobility, limited bandwidth, and high error rates. Traditional trust models fail to adapt to the dynamic underwater environment. Thus, to address these issues, we propose a dynamic trust evaluation and update model using a modified decision tree algorithm. Unlike baseline methods, which often rely on static and generalized trust evaluation approaches, our model introduces several innovations tailored specifically for UWSNs. These include energy-aware decision-making, real-time adaptation to environmental changes, and the integration of multiple underwater-specific factors such as water currents and acoustic signal properties. Our model enhances trust accuracy, reduces energy consumption, and lowers data overhead, achieving a 96% accuracy rate with a 2% false positive rate. Additionally, it outperforms baseline models by improving energy efficiency by 50 mW and reducing response time to 20 ms per packet. These innovations demonstrate the proposed model's effectiveness in addressing the unique challenges of UWSNs, ensuring both security and operational efficiency goals. The proposed model effectively enhances the trust evaluation process in UWSNs, providing both security and operational benefits. These key findings validate the potential of integrating modified decision tree algorithms to improve the performance and sustainability of UWSNs.

Development of decision tree classification algorithms in predicting mortality of COVID-19 patients.

INTRODUCTION: The accurate prediction of COVID-19 mortality risk, considering influencing factors, is crucial in guiding effective public policies to alleviate the strain on the healthcare system. As such, this study aimed to assess the efficacy of decision tree algorithms (CART, C5.0, and CHAID) in predicting COVID-19 mortality risk and compare their performance with that of the logistic model. METHODS: This retrospective cohort study examined 5080 cases of COVID-19 in Babol, a city in northern Iran, who tested positive for the virus via PCR from March 2020 to March 2022. In order to check the validity of the findings, the data was randomly divided into an 80% training set and a 20% testing set. The prediction models, such as Logistic regression models and decision tree algorithms, were trained on the 80% training data and tested on the 20% testing data. The accuracy of these methods for the test samples was assessed using measures like ROC curve, sensitivity, specificity, and AUC. RESULTS: The findings revealed that the mortality rate for COVID-19 patients who were admitted to hospitals was 7.7%. Through cross validation, it was determined that the CHAID algorithm outperformed other decision tree and logistic regression algorithms in specificity, and precision but not sensitivity in predicting the risk of COVID-19 mortality. The CHAID algorithm demonstrated a specificity, precision, accuracy, and F-score of 0.98, 0.70, 0.95, and 0.52 respectively. All models indicated that factors such as ICU hospitalization, intubation, age, kidney disease, BUN, CRP, WBC, NLR, O2 sat, and hemoglobin were among the factors that influenced the mortality rate of COVID-19 patients. CONCLUSIONS: The CART and C5.0 models had outperformed in sensitivity but CHAID demonstrates a better performance compared to other decision tree algorithms in specificity, precision, accuracy and shows a slight improvement over the logistic regression method in predicting the risk of COVID-19 mortality in the population under study.

Towards an automated classification phase in the software maintenance process using decision tree.

The software maintenance process is costly, accounting for up to 70% of the total cost in the software development life cycle (SDLC). The difficulty of maintaining software increases with its size and complexity, requiring significant time and effort. One way to alleviate these costs is to automate parts of the maintenance process. This research focuses on the automation of the classification phase using decision trees (DT) to sort, rank, and accept/reject maintenance requests (MRs) for mobile applications. Our dataset consisted of 1,656 MRs. We found that DTs could automate sorting and accepting/rejecting MRs with accuracies of 71.08% and 64.15%, respectively, though ranking accuracy was lower at 50%. While DTs can reduce costs, effort, and time, human verification is still necessary.

Deep Learning for Automatic Knee Osteoarthritis Severity Grading and Classification.

Introduction: Knee osteoarthritis (OA) is a prevalent condition that significantly impacts the quality of life, often leading to the need for knee replacement surgery. Accurate and timely identification of knee degeneration is crucial for effective treatment and management. Traditional methods of diagnosing OA rely heavily on radiological assessments, which can be time-consuming and subjective. This study aims to address these challenges by developing a deep learning-based method to predict the likelihood of knee replacement and the Kellgren-Lawrence (KL) grade of knee OA from X-ray images. Methodology: We employed the Osteoarthritis Initiative (OAI) dataset and utilized a transfer learning approach with the Inception V3 architecture to enhance the accuracy of OA detection. Our approach involved training 14 different models-Xception, VGG16, VGG19, ResNet50, ResNet101, ResNet152, ResNet50V2, ResNet101V2, ResNet152V2, Inception V3, Inception, ResNetV2, DenseNet121, DenseNet169, DenseNet201-and comparing their performance. Results: The study incorporated pixel ratio computation and picture pre-processing, alongside a decision tree model for prediction. Our experiments revealed that the Inception V3 model achieved the highest training accuracy of 91% and testing accuracy of 67%, with notable performance in both training and validation phases. This model effectively identified the presence and severity of OA, correlating with the Kellgren-Lawrence scale and facilitating the assessment of knee replacement needs. Conclusion: By integrating advanced deep learning techniques with radiological diagnostics, our methodology supports radiologists in making more accurate and prompt decisions regarding knee degeneration. The Inception V3 model stands out as the optimal choice for knee X-ray analysis, contributing to more efficient and timely healthcare delivery for patients with knee osteoarthritis.

Factors and prediction model associated with resumption of social outings in stroke survivors 6 months after onset: a multicenter prospective study.

OBJECTIVES: To identify factors associated with the resumption of social outings 6 months after stroke onset and develop a simple clinically practical prediction model. MATERIALS AND METHODS: Participants were recruited from first-ever stroke survivors admitted to three rehabilitation wards, and resumption of social outings 6 months after stroke onset was assessed using the Japanese version of the Frenchay Activities Index. The association of physical and cognitive functions with activities of daily living at admission to the rehabilitation ward and resumption of social outings 6 months after stroke onset was examined using logistic regression and decision trees. RESULTS: Notably, 63.2% of the 57 stroke survivors who participated in this study had lower Frenchay Activities Index scores for social outings 6 months after stroke onset than before. Logistic regression analysis revealed that attention deficit and grooming on the Functional Independence Measure (FIMTM) were significantly associated with decreased social outing scores 6 months after stroke onset. A decision tree model was created to predict the resumption of social outings using the presence or absence of attention disorders and FIMTM grooming score (>2 or ≤ 2). CONCLUSIONS: The results of this study suggest that attention deficit and beyond a certain level of independence in grooming (FIMTM >2) at admission to the rehabilitation ward are associated with recovery to the pre-stroke level of social outings 6 months after stroke onset. The decision tree created in this study holds promise as a simple model to predict the resumption of social outings among stroke survivors.

Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke.

OBJECTIVE: To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after intention-driven robotic hand training using residual voluntary electromyography (EMG) signals from affected UE. DESIGN: A prospective longitudinal multicenter cohort study. We collected preintervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from 8 time windows during MVC-EMG (0.1-5s) to identify subjects' motor intention. Classification and regression tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor improvements was further investigated. SETTING: Nine rehabilitation centers. PARTICIPANTS: Chronic stroke survivors (N=131), including 87 for derivation sample, and 44 for validation sample. INTERVENTIONS: All participants underwent 20-session robotic hand training (40min/session, 3-5sessions/wk). MAIN OUTCOME MEASURES: Prediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy. RESULTS: The best model comprised FMAUE (cutoff score, 46) and peak activity of ED from 1-second MVC-EMG (MVC-EMG 4.604 times higher than resting EMG), which demonstrated significantly higher prediction accuracy (AUC, 0.807) than other time windows or solely using clinical scores (AUC, 0.595). In external validation, this model displayed robust prediction (AUC, 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases. CONCLUSIONS: This study presents a prediction model for intention-driven robotic hand training in chronic stroke survivors. It highlights significance of capturing motor intention through 1-second EMG window as a predictor for MCID improvement in UE motor function after 20-session robotic training. Survivors in 2 conditions showed high percentage of clinical motor improvement: moderate-to-high motor intention and low-to-moderate function; as well as high intention and high function.

Development of predictive model for post-stroke depression at discharge based on decision tree algorithm: A multi-center hospital-based cohort study.

OBJECTIVE: Post-stroke depression (PSD) is one of the most common and severe neuropsychological sequelae after stroke. Using a prediction model composed of multiple predictors may be more beneficial than verifying the predictive performance of any single predictor. The primary objective of this study was to construct practical prediction tools for PSD at discharge utilizing a decision tree (DT) algorithm. METHODS: A multi-center prospective cohort study was conducted from May 2018 to October 2019 and stroke patients within seven days of onset were consecutively recruited. The independent predictors of PSD at discharge were identified through multivariate logistic regression with backward elimination. Classification and regression tree (CART) algorithm was employed as the DT model's splitting method. RESULTS: A total of 876 stroke patients who were discharged from the neurology departments of three large general Class A tertiary hospitals in Wuhan were eligible for analysis. Firstly, we divided these 876 patients into PSD and non-PSD groups, history of coronary heart disease (OR = 1.835; 95 % CI, 1.106-3.046; P = 0.019), length of hospital stay (OR = 1.040; 95 % CI, 1.013-1.069; P = 0.001), NIHSS score (OR = 1.124; 95 % CI, 1.052-1.201; P = 0.001), and Mini mental state examination (MMSE) score (OR = 0.935; 95 % CI, 0.893-0.978; P = 0.004) were significant predictors. The subgroup analysis results have shown that hemorrhagic stroke, history of hypertension and higher modified Rankin Scale score (mRS) score were associated with PSD at discharge in the young adult stroke patients. CONCLUSIONS: Several predictors of PSD at discharge were identified and convenient DT models were constructed to facilitate clinical decision-making.

Multi-Elemental Analysis and Geographical Discrimination of Greek "Gigantes Elefantes" Beans Utilizing Inductively Coupled Plasma Mass Spectrometry and Machine Learning Models.

Greek giant beans, also known as "Gigantes Elefantes" (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek "Gigantes Elefantes" beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model's effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek "Gigantes Elefantes" beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition.

Efficient Detection of Malicious Traffic Using a Decision Tree-Based Proximal Policy Optimisation Algorithm: A Deep Reinforcement Learning Malicious Traffic Detection Model Incorporating Entropy.

With the popularity of the Internet and the increase in the level of information technology, cyber attacks have become an increasingly serious problem. They pose a great threat to the security of individuals, enterprises, and the state. This has made network intrusion detection technology critically important. In this paper, a malicious traffic detection model is constructed based on a decision tree classifier of entropy and a proximal policy optimisation algorithm (PPO) of deep reinforcement learning. Firstly, the decision tree idea in machine learning is used to make a preliminary classification judgement on the dataset based on the information entropy. The importance score of each feature in the classification work is calculated and the features with lower contributions are removed. Then, it is handed over to the PPO algorithm model for detection. An entropy regularity term is introduced in the process of the PPO algorithm update. Finally, the deep reinforcement learning algorithm is used to continuously train and update the parameters during the detection process, and finally, the detection model with higher accuracy is obtained. Experiments show that the binary classification accuracy of the malicious traffic detection model based on the deep reinforcement learning PPO algorithm can reach 99.17% under the CIC-IDS2017 dataset used in this paper.

Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%.

Prediction model of weight control experience in men with obesity in their 30 s and 40 s using decision tree analysis.

Obesity is an abnormal and potentially dangerous condition caused by excess body fat accumulation. The number of people with obesity is increasing worldwide. Obesity is the primary cause of various diseases; therefore, it is crucial to make efforts to control body weight. Identifying the factors that influence men with obesity to attempt to control and not control their weight is essential. The objective of this study was to create a prediction model for weight control experience among Korean men in their 30 s and 40 s. We analyzed data from the 2022 Community Health Survey and included 12,311 men who were overweight or obese. The men were divided into two groups based on their weight control experience: (1) Yes group (n = 9405) and (2) No group (n = 2906). Chi-square and independent t-tests were used to compare general and health-related characteristics between the groups. Decision tree analysis was used to build a prediction model for weight control experience. A split-sample test was conducted to validate the model. From the results of this study, various models predicting weight control experience were derived. From the decision tree model without setting the first node, those who weighed below average, had a high school diploma or less, and did not know their blood sugar levels had the highest probability of not controlling their weight at 55.3%. In the prediction model where the first node was set to age, those in their 40 s who thought their weight was below average and were unaware of their blood sugar levels had the highest rate of not trying to control their weight at 50.1%. In the prediction model where the first node was set to BMI, those who were overweight but thought their weight was below average and had a high school diploma or less had the highest rate of not trying to control their weight at 51.5%. There is an urgent need to provide obesity prevention and management education to those who have no weight control experience, particularly those at high risk, as identified in this study.