Support Vector Machine Regression Algorithm Research Articles

Improving Alzheimer's Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques.

Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer's disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Machine Learning-Based Strategies for Detecting Cyberbullying in Online Chats

This study employed the stacking of three machine learning techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression algorithms to develop a model for detecting cyberbullying using a post dataset acquired from the X Platform. The proposed model's task is to extract keywords from the post dataset and then classify them as either 1 ("cyberbullying word") or 0 ("not cyberbullying word"). The model generated an accuracy of 85.52%, and it was deployed using a simple Graphical User Interface (GUI) web application. This study recommends that the model be included on social media platforms to help reduce the growing use of cyberbullying phrases.

Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions

The rapid and accurate estimation of leaf chlorophyll content (LCC), an important indicator of crop photosynthetic capacity and nutritional status, is of great significance for precise nitrogen fertilization management. To explore the existence of a versatile regression model that can be successfully used to estimate the LCC for different varieties under different growth stages and nitrogen stress conditions, a study was conducted in 2023 across the growing season for winter wheat with five species and five nitrogen application levels. Two machine learning regression algorithms, support vector machine (SVM) and random forest (RF), were used to establish the bridge between UAV-derived multispectral vegetation indices and ground truth LCC (relative chlorophyll content, SPAD), taking the multivariate linear regression (MLR) algorithm as a reference. The results show that the visible atmospherically resistant index, vegetative index, and normalized difference vegetation index had the highest correlation with ground truth LCC, with a Pearson’s correlation coefficient of 0.95. All three regression algorithms (MLR, RF, and SVM) performed well on the training dataset (R2: 0.932–0.944, RMSE: 3.96 to 4.37), but performed differently on validation datasets with different growth stages, species, and nitrogen application levels. Compared to winter wheat species and nitrogen application levels, the growth stages had the greatest influence on the generalization ability of LCC estimation models, especially for the dough stage. At the dough stage, compared to MLR and RF, SVM performed best, with R2 increasing by 0.27 and 0.10, respectively, and RMSE decreasing by 1.13 and 0.46, respectively. Overall, this study demonstrated that the combination of UAV-derived multispectral VIs and the SVM regression algorithm could be successfully applied to map the LCC of winter wheat for different species, growth stages, and nitrogen stress conditions. Ultimately, this research is significant as it shows the successful application of UAV data for mapping the LCC of winter wheat across diverse conditions, offering valuable insights for precision nitrogen fertilization management.

FRT capability improvement of a wind power system using support vector regression and deep neural network models

ABSTRACT The increasing energy demand and environmental conditions have led many countries to favor renewable energy,such as wind and solar,over conventional power plants. As per the Central Electricity Authority (CEA), India’s wind power capacity has grown substantially, reaching 45,154 MW in 2023, from 22,465 MW in 2013. However, integrating wind generators to an existing power system network can reduce transient stability during faults. To prevent wind turbine disconnection during faults, Indian grid code authorities mandate Fault Ride Through (FRT) or Low Voltage Ride Through (LVRT) capability for wind turbines. This paper proposes Dynamic Voltage Restorer (DVR) for enhancing the FRT capability of a constant speed wind turbine. Analysis and simulation are done on a fixed speed wind turbine employing Squirrel Cage Induction Generator (SCIG) under balanced and unbalanced voltage sags. Performance of DVR with two Artificial Intelligence-based controllers, Support Vector Machines(SVM) Regression Model Based Supervised Machine Learning algorithm, and Deep Neural Network (DNN) controller, is analysed and compared. The SVM Regression algorithm, achieves 100% voltage sag compensation under unbalanced voltage sags, reduces torque pulsations to 25% and maintains rotor speed at rated values, enabling the turbine to remain connected to the grid. Additionally it reduces Total Harmonic Distortion to 2%.

Comparison of traditional regression modeling vs. AI modeling for the prediction of dental caries: a secondary data analysis

IntroductionThere are substantial gaps in our understanding of dental caries in primary and permanent dentition and various predictors using newer modeling methods such as Machine Learning (ML) algorithms and Artificial Intelligence (AI). The objective of this study is to compare the accuracy, precision, and differences between the caries predictive capability of AI vs. traditional multivariable regression techniques.MethodsThe study was conducted using secondary data stored in the Temple University Kornberg School of Dentistry electronic health records system (axiUm) of pediatric patients aged 6–16 years who were patients on record at the Pediatric Dentistry Clinic. The outcome variables considered in the study were the decayed–missing–filled teeth (DMFT) and the decayed–extracted–filled teeth (deft) scores. The predictors included age, sex, insurance, fluoride exposure, having a dental home, consumption of sugary meals, family caries experience, having special needs, visible plaque, medications reducing salivary flow, and overall assessment questions.ResultsThe average DMFT score was 0.85 ± 2.15, while the average deft scores were 0.81 ± 2.15. For childhood dental caries, XGBoost was the best performing ML algorithm with accuracy, sensitivity. and Kappa as 81%, 84%, and 61%, respectively, followed by Support Vector Machine and Lasso Regression algorithms, both with 84% specificity. The most important variables for prediction found were age and visible plaque.ConclusionsThe machine learning model outperformed the traditional statistical model in the prediction of childhood dental caries. Data from a more diverse population will help improve the quality of caries prediction for permanent dentition where the traditional statistical method outperformed the machine learning model.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Data-driven battery health state estimation based on charging and switching cabinets

Accurate evaluation of the battery’s state of health (SOH) in the charging and switching cabinet is crucial to ensure the battery operates safely and reliably, while also reducing maintenance costs for the battery system. A support vector regression technique, utilising the sparrow algorithm optimisation, is suggested to improve the precision of assessing the battery’s state of health in the charging and switching cabinet. This algorithm tackles the task of identifying parameters in the conventional model. Analysing the ageing dataset of lithium batteries is the initial stage to determine the health parameters that signal the battery’s health state. The support vector machine regression algorithm is employed to select the kernel function and penalty factor, which are fine-tuned using the sparrow optimisation technique. The data is utilised to create the SSA-SVR model. The battery health status of the charging and switching cabinet is assessed. The study shows that the enhanced support vector regression model can effectively monitor the status of lithium-ion batteries and achieve superior estimation results for different types of batteries.

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

In the context of democracy in Indonesia, elections play a crucial role, and survey agencies often publish their results on social media. User responses, especially from voters, often express dissatisfaction, including distrust, insults, and negative comments, if the candidate they support receives low survey results. Therefore, this study aims to examine the level of public trust in the survey results of Presidential candidates in 2024 using the Support Vector Machine (SVM) and Logistic Regression algorithms. The study utilized data from 1778 Instagram comments and 985 Twitter tweets. The process involved problem identification, data collection, and system implementation, such as preprocessing, labeling, SMOTE, TF-IDF, data splitting, model classification, and evaluation. The results show that SVM with an 80% training data and 20% test data scenario provides high accuracy, namely 93.19% from Instagram and 91.19% from Twitter. Logistic Regression, with the highest accuracy of 89.79% from Instagram and 88.01% from Twitter in the same scenario. Sentiment analysis using SVM scenario one resulted in 195 positive comments and 216 negative comments. Logistic Regression scenario one shows 180 positive sentiments and 216 negative sentiments. From the classification results, it can be concluded that the level of public trust tends to be negative towards the survey results of the 2024 Presidential candidates, both using SVM and Logistic Regression.

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Assessing occupational hazards in welding operations: A machine learning-based approach for worker safety in Indian foundries.

Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety regulations, especially in developing countries with inadequate adherence to safety standards and resources. This study employs machine learning for predicting ultraviolet radiation and noise levels during welding, emphasizing worker safety. The focus is on the Indian foundry sector to gauge actual exposure vis-á-vis safety standards. Ultraviolet radiation and noise emitted during the welding of a ferrous alloy were collected from three foundries in Agra, India. Five machine learning (ML) algorithms were applied for data analysis and prediction of UV radiation and noise levels, and a relative performance comparison was carried out on the compiled data against safety standards. Out of all the ML algorithms applied, the Support Vector Machine regression algorithm (RMSE = 356.93) obtained the best performance on UV radiation data, and the Random Forest algorithm (RMSE = 11.4) was found to give the best results for the noise level prediction task. This work represents the first known application of machine learning techniques for predicting UV radiation and noise levels in arc welding processes. The results show the efficacy of algorithms such as SVM regression and Random Forest for the problem. Further, the datasets and ML algorithms implemented in the work will be made openly available to support further research endeavors in this and related areas.

Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

Machine Learning Based Analysis and Prediction of Emotional Expressions in Dance Movements

Abstract Dance movement is a powerful way to convey human emotions, and analyzing and predicting the emotional expression of dance movement through machine learning has become a hotspot in the field of artificial intelligence research nowadays. This paper employs the Seagull optimization algorithm to enhance the SVM classification model, laying the algorithmic groundwork for the research, and refines it to align with the research requirements. The classification of dance movements is accomplished by the nonlinear regression algorithm in the support vector machine regression algorithm, while the task of capturing dance movements is realized by means of Euler angles to describe the orientation, rotation matrices to transform vectors between different coordinate systems, and quaternions to optimize the Euler angles. Finally, this paper analyses and predicts the emotional expression of dance movements using a classification loss model (LSTMBO) and a W-RNN model that incorporates the weights of emotion words. In this paper, tests on the classification algorithm revealed that the algorithm's classification accuracy is above 90% for all datasets used in the research. Moreover, the performance and effectiveness of dance action capture are significantly better than other comparative algorithms. Simultaneously, this paper's algorithm achieves an accuracy of over 80% in predicting emotions expressed through dance movements. Numerous experiments have proven the effectiveness and superiority of the algorithm model in this paper, thereby promoting the research and development of the field.

Study on prediction of early adverse events by CapeOX therapy in patients with colorectal cancer.

CapeOX is a regimen used as postoperative adjuvant chemotherapy for the treatment of advanced recurrent colorectal cancer. If early adverse events occur, treatment may not progress as planned and further dose reduction may be necessary. In this study, we investigated whether pre-treatment medical records could be used to predict adverse events in order to prevent adverse events caused by CapeOX treatment. The 178 patients were classified into two groups (97 in the adverse event positive group and 81 in the adverse event-negative group) based on withdrawal or postponement of four or fewer courses. In univariate analysis, age, height, weight, body surface area (BSA), creatinine clearance, muscle mass, and lean body mass were associated with early adverse events (P<0.05). The area under the receiver operating characteristic curve obtained by Stepwise logistic regression analysis using the Akaike information criterion method was 0.832. For nested k-fold cross validation, the accuracy rates of the support vector machine, random forest, and logistic regression algorithms were 0.71, 0.70, and 0.75, respectively. The results of the present study suggest that a logistic regression prediction model may be useful in predicting early adverse events caused by CapeOX therapy in patients with colorectal cancer. J. Med. Invest. 71 : 141-147, February, 2024.

Tensile strength analysis of additively manufactured CM 247LC alloy specimen by employing machine learning classifiers.

Using a cutting-edge net-shape manufacturing technique called Additive Layer Manufacturing (ALM), highly complex components that are not achievable with conventional wrought and cast methods can be produced. As a result, the aerospace sector is paying closer attention to using this technology to fabricate superalloys based on nickel to develop the holistic gas turbine. Because of this, there is an increasing need for the mechanical characterisation of such material. Conventional mechanical testing is hampered by the limited availability of material that has been processed, especially given the large number of process factors that need to be assessed. Thus, the present study focuses on manufacturing CM247LC Ni-based superalloy with exceptional mechanical characteristics by laser powder bed fusion (L-PBF). This study evaluates the effect of input process variables such as laser power, scan speed, hatch distance and volumetric energy density on the mechanical performance of the LPBF CM247LC superalloy. The maximum value of as-built tensile strength obtained in the study is 997.81 MPa. Plotting Pearson's heatmap and the Feature importance (F-test) was used in the data analysis to examine the impact of input parameters on tensile strength. The accuracy of the tensile strength data classification by machine learning algorithms, such as k-nearest neighbours, Naïve Baiyes, Support vector machine, XGBoost, AdaBoost, Decision tree, Random forest, and logistic regression algorithms, was 92.5%, 83.75%, 83%, 85%, 87.5%, 90%, 91.25%, and 77.5%, respectively.

Integrated analysis reveals the potential of cluster of differentiation 86 as a key biomarker in high-grade glioma.

This study aimed to evaluate the potential of cluster of differentiation 86 (CD86) as a biomarker in high-grade glioma (HGG). The TCGA and TCIA databases were used to obtain the CD86 expression value, clinical data, and MRI images of HGG patients. Prognostic values were assessed by the Kaplan-Meier method, Receiver operating characteristic curve (ROC), Cox regression, logistic regression, and nomogram analyses. CD86-associated pathways were also explored. We found that CD86 was significantly upregulated in HGG compared with the normal group. Survival analysis showed a significant association between CD86 high expression and shorter overall survival time. Its independent prognostic value was also confirmed. These results suggested the possibility of CD86 as a biomarker in HGG. We also innovatively established 2 radiomics models with Support Vector Machine (SVM) and Logistic regression (LR) algorithms to predict the CD86 expression. The 2 models containing 5 optimal features by SVM and LR methods showed similar favorable performance in predicting CD86 expression in the training set, and their performance were also confirmed in validation set. These results indicated the successful construction of a radiomics model for non-invasively predicting biomarker in HGG. Finally, pathway analysis indicated that CD86 might be involved in the natural killer cell-mediated cytotoxicity in HGG progression.

Polynomial surface-fitting evaluation of new energy maximum power generation capacity based on random forest association analysis and support vector regression

Focusing on frequency problems caused by wind power integration in ultra-high-voltage DC systems, an accurate assessment of the maximum generation capacity of large-scale new energy sources can help determine the available frequency regulation capacity of new energy sources and improve the frequency stability control of power systems. First, a random forest model is constructed to analyze the key features and select the indexes significantly related to the generation capacity to form the input feature set. Second, by establishing an iterative construction model of the polynomial fitting surface, data are maximized by the upper envelope surface, and an effective sample set is constructed. Furthermore, a new energy maximum generation capacity assessment model adopts the support vector machine regression algorithm under the whale optimization algorithm to derive the correspondence between the input features and maximum generation capacity of new energy sources. Finally, we validate the applicability and effectiveness of the new maximum energy generation capacity evaluation model based on the results of an actual wind farm.

Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (Rp), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea.

Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics.

To generate an image-driven biomarker (Rad_score) to predict tumor-infiltrating regulatory T lymphocytes (Treg) in breast cancer (BC). Overall, 928 BC patients were enrolled from the Cancer Genome Atlas (TCGA) for survival analysis; MRI (n = 71 and n = 30 in the training and validation sets, respectively) from the Cancer Imaging Archive (TCIA) were retrieved and subjected to repeat least absolute shrinkage and selection operator for feature reduction. The radiomic scores (rad_score) for Treg infiltration estimation were calculated via support vector machine (SVM) and logistic regression (LR) algorithms, and validated on the remaining patients. Landmark analysis indicated Treg infiltration was a risk factor for BC patients in the first 5 years and after 10 years of diagnosis (p = 0.007 and 0.018, respectively). Altogether, 108 radiomic features were extracted from MRI images, 4 of which remained for model construction. Areas under curves (AUCs) of the SVM model were 0.744 (95% CI 0.622-0.867) and 0.733 (95% CI 0.535-0.931) for training and validation sets, respectively, while for the LR model, AUCs were 0.771 (95% CI 0.657-0.885) and 0.724 (95% CI 0.522-0.926). The calibration curves indicated good agreement between prediction and true value (p > 0.05), and DCA shows the high clinical utility of the radiomic model. Rad_score was significantly correlated with immune inhibitory genes like CTLA4 and PDCD1. High Treg infiltration is a risk factor for patients with BC. The Rad_score formulated on radiomic features is a novel tool to predict Treg abundance in the tumor microenvironment.