Decision Tree Classifier Research Articles

Developmental and Validation of Machine Learning Model for Prediction Complication after Cervical Spine Metastases Surgery.

This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery. The main objective is to develop a machine learning model that accurately predicts complications following cervical spine metastases surgery. Cervical spine metastases surgery can enhance quality of life but carries a risk of complications influenced by various factors. Existing scoring systems may not include all predictive factors. Machine learning offers the potential for a more accurate predictive model by analyzing a broader range of variables. Data from January 2012 to December 2020 were retrospectively collected from medical databases. Predictive models were developed using Gradient Boosting, Logistic Regression, and Decision Tree Classifier algorithms. Variables included patient demographics, disease characteristics, and laboratory investigations. SMOTE was used to balance the dataset, and the models were assessed using AUC, F1-score, precision, recall, and SHAP values. The study included 72 patients, with a 29.17% postoperative complication rate. The Gradient Boosting model had the best performance with an AUC of 0.94, indicating excellent predictive capability. Albumin level, platelet count, and tumor histology were identified as top predictors of complications. The Gradient Boosting machine learning model showed superior performance in predicting postoperative complications in cervical spine metastases surgery. With continuous data updating and model training, machine learning can become a vital tool in clinical decision-making, potentially improving patient outcomes. Level III.

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%.

Optimized Prescreen Survey Tool for Predicting Sleep Apnea Based on Deep Neural Network: Pilot Study

Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models.

LOGISTIC REGRESSION TECHNIQUE FOR CARDIOVASCULAR DISEASE PREDICTION

Cardiovascular diseases (CVDs) are the most common cause of death in the world. Over four out of five CVD deaths are due to heart attacks and strokes. CVD high mortality has led to about 17 million deaths worldwide. Several machine and deep learning techniques are used to classify the presence and absence of CVD. This paper presents a logistic regression (LR) technique for predicting the risk of heart diseases (HD). The goal is to create an LR algorithm and build a prediction model that would foretell the development of HD. The dataset included data on 207 patients, featuring the following: age, sex, chest pain type, blood pressure, cholesterol levels, fasting blood sugar > 120 mg/dl, electrocardiogram results, maximum heart rate, exercise-induced angina, ST depression, slope of the ST segment, number of major vessels colored by fluoroscopy, and thallium scan results. Using this dataset to train the LR technique, a robust model was created to accurately predict the existence of HD in new patients. With an accuracy of 81%, a precision of 83%, and a recall score of 76%, the accuracy, precision, and recall key metrics were used to evaluate the model's efficacy. The model’s accuracy was compared to alternative methods, such as K-Nearest Neighbors and Decision Tree classifiers, which yielded accuracy of 81% and 76%, respectively. The obtained results are of great significance for healthcare providers – the proposed model can assist in identifying those who are at high risk of heart diseases and allow for early implementation of prophylactic...

Stacked Classification Approach using Optimized Hybrid Deep Learning Model for Early Prediction of Behaviour Changes on Social Media

Detecting signs of suicidal thoughts on social media is paramount for preventing suicides, given the platforms' role as primary outlets for emotional expression. Traditional embedding techniques focus solely on semantic analysis and lack the sentiment analysis essential for capturing emotions. This limitation poses challenges in developing high-accuracy models. Additionally, previous studies often rely on a single dataset, further constraining their effectiveness. To overcome these challenges, this study proposes an innovative approach that integrates embedding techniques such as BERT, which offers semantic and syntactic analysis of the posts, with sentiment analysis provided by VADER scores extracted from the VADER sentiment analysis tool. The identified features are then input into the proposed optimized hybrid deep learning model, specifically the Bi-GRU and Attention incorporated with Stacked or stacking Classifier (Decision Tree, Random Forest, Gradient Boost, as the base classifier and XGBoost as meta classifier), which undergoes optimization using the grid search technique to enhance detection capabilities. In evaluations, the model achieved an impressive accuracy and F1-score of 98% on the Reddit dataset and 97% on the Twitter dataset. The research evaluates the efficacy of several machine learning models, encompassing Decision Trees, Random Forests, Gradient Boosting, and XGBoost. Moreover, it examines sophisticated models like LSTM with Attention, Bi-LSTM with Attention, and Bi-GRU with Attention, augmented with word embeddings such as BERT, MUSE, and fastText, alongside the fusion of sentiment VADER score. These results emphasize the promise of a holistic strategy that combines advanced feature embedding techniques with semantic features, showcasing a notably efficient detection of suicidal ideation on social media.

Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs

The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutics given the lack of specificity with symptom-based identification of LC and the overlap of symptoms with other chronic inflammatory conditions. Here, we report a machine-learning approach to LC/PASC diagnosis on 347 individuals using cytokine hubs that are also capable of differentiating LC from chronic lyme disease (CLD). We derived decision tree, random forest, and gradient-boosting machine (GBM) classifiers and compared their diagnostic capabilities on a dataset partitioned into training (178 individuals) and evaluation (45 individuals) sets. The GBM model generated 89% sensitivity and 96% specificity for LC with no evidence of overfitting. We tested the GBM on an additional random dataset (106 LC/PASC and 18 Lyme), resulting in high sensitivity (97%) and specificity (90%) for LC. We constructed a Lyme Index confirmatory algorithm to discriminate LC and CLD.

PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.

Optimizing Breast Cancer Diagnosis with Machine Learning Algorithms

Breast cancer is the most common disease among women worldwide, and it continues to pose a serious threat to global health [2]. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. Traditional diagnostic methods, while effective, have limitations in consistency and speed. Medical diagnostics have undergone a revolution with the introduction of machine learning (ML), which provides tools to analyze complex data and increase diagnosis accuracy. Using the Breast Cancer Wisconsin (Diagnostic) Dataset[1], this investigation dives into the application of several machine learning (ML) algorithms to improve the effectiveness of the diagnosis of breast cancer in terms of accuracy. Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Support Vector Classifier (SVC), XGBoost Classifier (XGBC), and Convolutional Neural Network (CNN) are used. Improved performance metrics were obtained across models by applying optimization approaches, particularly genetic algorithm for the selection of features, following preprocessing and initial training. RandomForestClassifier, XGBClassifier, and CNN notably showed significant enhancements in accuracy, ROC AUC score, recall, precision, and F1 score post-optimization. Ensemble methods and deep learning architectures proved effective in handling complex data and reducing overfitting, with Random Forest Classifier standing out as consistently superior. Conversely, Decision Tree Classifier and Support Vector Classifier exhibited mixed results, showcasing the importance of optimization strategies. This research shows the capability of ML in augmenting breast cancer diagnostics, advocating for further exploration of ensemble methods and advanced neural networks to refine diagnostic tools and improve patient outcomes.

Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment

Purpose This study aims to evaluate the efficacy of various machine learning models in predicting heart failure incidence using medical data, focusing on the innovative aspect of a novel dataset. Unlike previous studies that predominantly examined features such as smoking status or age, this research explores novel features. The primary challenge addressed is the utilization of these new features, coupled with machine learning techniques, to accurately diagnose heart failure. Methods Five machine learning models, including logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors, were applied to analyze medical data from a dataset comprising over 900 individuals. The dataset encompasses diverse parameters such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, and electrocardiogram results, introducing a novel approach to feature selection. Results The evaluation of machine learning models unveiled varying performances in predicting heart failure. Logistic regression and support vector classifier exhibited the highest accuracy of 88%, followed by the decision tree classifier (below 85%), random forest classifier (84%), and K-nearest neighbors (82%). Additionally, the analysis revealed a balanced dataset distribution and highlighted sex-based disparities in heart failure incidence, along with significant correlations with factors such as age, chest pain severity, blood glucose levels, and physical activity. Conclusions The findings underscore the potential of integrating multiple machine learning models for early detection of heart failure, leveraging the inclusion of novel features in the dataset. However, careful model selection is crucial to account for discrepancies in accuracy among different models, emphasizing the importance of tailoring approaches based on specific project requirements.

UTILIZING NUTRITIONAL AND LIFESTYLE DATA FOR PREDICTING STUDENT ACADEMIC PERFORMANCE: A MACHINE LEARNING APPROACH

Nutrition and lifestyle factors have an enormous impact on students' academic performance. Nonetheless, there is a shortage of machine learning models to predict students' academic performance based on their nutrition and lifestyle. This paper intends to fill those gaps based on an extensive dataset of various attributes, underlining the capabilities of advanced machine learning models in uncovering the complex relationship between nutrition, lifestyle factors and student’s academic performance. A cross-sectional study was conducted in Kalar Technical College, Garmian Polytechnic University in Kurdistan region - Iraq, that involved 500 undergraduate students whose ages range from 18 to 22 years old; the dataset contains demographic characteristics, dietary intake, physical activity, and anthropometric measurements. Various Techniques, tools, and machine learning algorithms such as logistic regression and decision tree classifiers were employed using Python's Scikit-Learn library; finally, Pre-processing of the data was carried out to ensure its suitability for analysis. The machine learning model that the authors developed showed promising prediction results. While the logistic regression model had an accuracy of 70.4%, the decision tree model excelled with an accuracy of 98.55%. Furthermore, exercise, BMI, and dietary intake notably impacted students’ academic performance.

Ensemble of Deep Learning Architectures with Machine Learning for Pneumonia Classification Using Chest X-rays.

Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images. We deployed modified VGG19, ResNet50V2, and DenseNet121 models for feature extraction, followed by five machine learning classifiers (logistic regression, support vector machine, decision tree, random forest, artificial neural network). The approach we suggested displayed remarkable accuracy, with VGG19 and DenseNet121 models obtaining 99.98% accuracy when combined with random forest or decision tree classifiers. ResNet50V2 achieved 99.25% accuracy with random forest. These results illustrate the advantages of merging deep learning models with machine learning classifiers in boosting the speedy and accurate identification of pneumonia. The study underlines the potential of DLxMLC systems in enhancing diagnostic accuracy and efficiency. By integrating these models into clinical practice, healthcare practitioners could greatly boost patient care and results. Future research should focus on refining these models and exploring their application to other medical imaging tasks, as well as including explainability methodologies to better understand their decision-making processes and build trust in their clinical use. This technique promises promising breakthroughs in medical imaging and patient management.

GATree: Evolutionary decision tree classifier in Python

GATree: Evolutionary decision tree classifier in Python

An ensemble machine learning-based approach to predict cervical cancer using hybrid feature selection

Cervical cancer has recently emerged as the leading cause of premature death among women. Around 85% of cervical cancer cases occur in underdeveloped countries. There are several risk factors associated with cervical cancer. This study describes a novel predictive model that uses early screening and risk trends from individual health records to forecast cervical cancer patients' prognoses. This study uses machine learning classification techniques to investigate the risk factors for cervical cancer. Additionally, use the voting method to evaluate all models and select the most appropriate model. The dataset used in this study contains missing values and shows a significant imbalance. Thus, the Random Oversampling technique was used as a sampling method. We used Principal Component Analysis (PCA) and XGBoost feature selection techniques to determine the most important features. To predict the accuracy, we used several machine learning classifiers, including Support Vector Machines (SVM), Random Forest (RF), k-nearest Neighbors (KNN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), AdaBoost (AdB), Gradient Boosting (GB), Multilayer Perceptron (MLP), and Nearest Centroid Classifier (NCC). To demonstrate the efficacy of the suggested model, a comparison of its accuracy, sensitivity, and specificity was performed. We used the Random Oversampling approach along with the Ensemble ML method, hard voting on RF and MLP, and achieved 99.19% accuracy. It is demonstrated that the ensemble ML classifier (hard voting) performs better at handling classification problems when features are decreased and the high-class imbalance problem is handled.

A predictive model for progression to clinical arthritis in at-risk individuals with arthralgia based on lymphocyte subsets and ACPA.

The presence of autoantibodies against citrullinated proteins (ACPA) significantly increases the risk of developing rheumatoid arthritis (RA). Dysregulation of lymphocyte subpopulations was previously described in RA. To propose the predictive model for progression to clinical arthritis based on peripheral lymphocyte subsets and ACPA in individuals who are at risk of RA. Our study included 207 at-risk individuals defined by the presence of arthralgias and either additional ACPA positivity or meeting the EULAR definition for clinically suspect arthralgia. For the construction of predictive models, 153 individuals with symptom duration ≥12 months who have not yet progressed to arthritis were included. The lymphocyte subsets were evaluated using flow cytometry and anti-CCP using ELISA. Out of all individuals with arthralgia, 41 progressed to arthritis. A logistic regression model with baseline peripheral blood lymphocyte subpopulations and ACPA as predictors was constructed. The resulting predictive model showed that high anti-CCP IgG, higher percentage of CD4+ T cells, and lower percentage of T and NK cells increased the probability of arthritis development. Moreover, the proposed classification decision tree showed, that individuals having both high anti-CCP IgG and low NK cells have the highest risk of developing arthritis. We propose a predictive model based on baseline levels of lymphocyte subpopulations and ACPA to identify individuals with arthralgia with the highest risk of progression to clinical arthritis. The final model includes T cells and NK cells, which are involved in the pathogenesis of RA. This preliminary model requires further validation in larger at-risk cohorts.

The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients’ Records

BackgroundThe utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined.MethodRetrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables.ResultsFour thousand four hundred eighteen patients’ records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976.ConclusionOur study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost–benefit analysis, is necessary to fully evaluate routine preoperative ultrasound’s overall utility and economic impact in this patient population.Graphical

Application of C4.5 algorithm with PSO Feature Selection and Bagging Technique on Breast Cancer Classification

The second greatest cause of death for women worldwide is breast cancer. When abnormal cells in the body proliferate out of control, cancer is the result. The diagnosis of breast cancer was established using anthropometric data obtained from standard blood tests. From the UCI Machine Learning Repository, the Breast Cancer Coimbra Data Set was obtained and put to use. One popular classification decision tree method is the C4.5 approach. By selecting the appropriate features and applying the appropriate strategy to address class imbalance throughout the classification process, the performance of the C4.5 algorithm may be enhanced. To determine the accuracy of the classification, tests are conducted using a confusion matrix. Accuracy in this study is anticipated to increase with the application of the C4.5 Algorithm, the Bagging approach to address class imbalance, and the PSO feature selection method. The C4.5 Algorithm, PSO, Bagging Technique produce the best accuracy results, with an average of 86.36 percent. The C4.5 classification method has the second highest accuracy, with a PSO accuracy of 79.39 percent. Utilizing the Bagging Technique in conjunction with the C4.5 Algorithm, the accuracy of 75.0% is the third highest. Furthermore, it has a 65.71 percent accuracy with the C4.5 categorization. As a result, the increase in accuracy from before adding PSO and Bagging Technique was 20.65%, indicating that the inclusion of PSO and Bagging Technique had a substantial impact on the calculation process.

Automated system utilizing non-invasive technique mammograms for breast cancer detection

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Machine Learning for Breast Cancer Detection with Dual-Port Textile UWB MIMO Bra-Tenna System

A wearable textile bra-tenna system based on dual-polarization sensors for breast cancer (BC) detection is presented in this paper. The core concept behind our work is to investigate which type of polarization is most effective for BC detection, using the combination of orthogonal polarization signals with machine learning (ML) techniques to enhance detection accuracy. The bra-tenna sensors have a bandwidth ranging from 2–12 GHz. To complement the proposed system, detection based on machine learning algorithms (MLAs) is developed and tested to enhance its functionality. Using scattered signals at different polarizations, the bra-tenna system uses MLAs to predict BC in its early stages. Classification techniques are highly effective for data classification, especially in the biomedical field. Two scenarios are considered: Scenario 1, where the system detects a tumor or non-tumor, and Scenario 2, where the system detects three classes of one, two, and non-tumors. This confirms that MLAs can detect tumors as small as 10 mm. ML techniques, including eight algorithms such as the Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Methods (GBMs), Decision Tree (DT) classifier, Ada Boost (AD), CatBoost, Extreme Gradient Boosting (XG Boost), and Logistic Regression (LR), are applied to this balanced dataset. For optimal analysis of the BC, a performance evaluation is performed. Notably, SVM achieves outstanding performance in both scenarios, with metrics such as its F1 score, recall, accuracy, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and precision all exceeding 90%, helping doctors to effectively investigate BC. Furthermore, the Horizontal-Horizontal (HH) sensor configuration achieved the highest accuracy of 98% and 99% for SVMs in the two scenarios, respectively.

Analysis of passenger perception heterogeneity and differentiated service strategy for air-rail intermodal travel

Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives

AbstractCarbon emissions limit the output of traditional fuel‐fired generating units, significantly affecting the new power system scheduling mechanism. This paper proposes a short‐term electric power and energy balance optimization scheduling method with low‐carbon bilateral demand response (LCBDR). The LCBDR mechanism framework is constructed by combining the analysis of short‐term electric power and energy balance of the system under a dual perspective, along with the electric‐carbon coupling mechanism of the dynamic scheduling on the source‐load side. Based on the carbon emission flow (CEF) theory, the carbon emission index information of load‐side users is obtained. An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. Comprehensive case studies from three different perspectives verify that this method can effectively realize the low‐carbon economic operation of the system, with the peak net load reduced by 24.02% and valley net load increased by 20.43%. Compared with a single perspective, the total operational costs can be reduced by 5.27%, and the carbon emissions of users can be reduced by 5.70%.