Gradient Boosting Decision Tree Model Research Articles

Observation-Based Ozone Formation Rules by Gradient Boosting Decision Trees Model in Typical Chemical Industrial Parks

Observation-Based Ozone Formation Rules by Gradient Boosting Decision Trees Model in Typical Chemical Industrial Parks

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them. Adversarial attacks are attack samples produced by carefully manipulating the samples at the test time to violate the model integrity by causing detection mistakes. In this paper, we analyse the performance of five realistic target-based adversarial attacks, namely Extend, Full DOS, Shift, FGSM padding + slack and GAMMA, against two machine learning models, namely MalConv and LGBM, learned to recognise Windows Portable Executable (PE) malware files. Specifically, MalConv is a Convolutional Neural Network (CNN) model learned from the raw bytes of Windows PE files. LGBM is a Gradient-Boosted Decision Tree model that is learned from features extracted through the static analysis of Windows PE files. Notably, the attack methods and machine learning models considered in this study are state-of-the-art methods broadly used in the machine learning literature for Windows PE malware detection tasks. In addition, we explore the effect of accounting for adversarial attacks on securing machine learning models through the adversarial training strategy. Therefore, the main contributions of this article are as follows: (1) We extend existing machine learning studies that commonly consider small datasets to explore the evasion ability of state-of-the-art Windows PE attack methods by increasing the size of the evaluation dataset. (2) To the best of our knowledge, we are the first to carry out an exploratory study to explain how the considered adversarial attack methods change Windows PE malware to fool an effective decision model. (3) We explore the performance of the adversarial training strategy as a means to secure effective decision models against adversarial Windows PE malware files generated with the considered attack methods. Hence, the study explains how GAMMA can actually be considered the most effective evasion method for the performed comparative analysis. On the other hand, the study shows that the adversarial training strategy can actually help in recognising adversarial PE malware generated with GAMMA by also explaining how it changes model decisions.

Hardness-guided machine learning for tungsten alloy strength prediction

Yield strength determines the material's resistance to permanent deformation. However, the traditional estimation method based on the triple relationship of Vickers hardness exhibits limited accuracy when applied to tungsten heavy alloys (WHA) with two-phase structure. This study presents a novel approach using machine learning and hardness data to predict the yield strength of tungsten heavy alloy efficiently and accurately. Among the eight machine learning models used, Gradient boosting decision tree model (GBDT) was the most effective model. The results demonstrate a strong agreement between the GBDT-based predictions and experimental results, with an average error of 6.7 %. We also gave the hardness versus yield strength relationship for the 90WNiFe alloy system. This finding highlights the potential of the GBDT regression model in conjunction with hardness measurements for predicting yield strength with high precision.

Relationships between Resident Activities and Physical Space in Shrinking Cities in China—The Case of Chaoyang City

Shrinking cities suffer from a decreased level of resident activities. As a result, areas with low levels of resident activities may become breeding grounds for social issues. To ease and prevent social issues, it is important to deploy physical space optimisation strategies to effectively guide the distribution of resident activities in shrinking cities. To support the development of such spatial strategies, this paper introduces machine learning-based methods for analysing the nuanced non-linear relationship between resident activities and physical space in shrinking cities. Utilising dual-scale grids, this study calculates multi-source spatial elements, which are subsequently integrated with resident activity data to construct a gradient boosting decision tree model. It then analyses the weight of different spatial elements’ impacts on resident activities and their nonlinear relationships. The model proposed in this study demonstrates good precision in construing the relationship between resident activities and physical space. Based on the research findings, strategies for different types of spatial development in shrinking cities are drawn out. This paper advocates for the application of this analytical approach before conducting spatial planning in shrinking cities to maximise the effectiveness of spatial development in guiding resident activities.

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

A new FCM-XGBoost system for predicting Pavement Condition Index

Predicting Pavement Condition Index (PCI) is crucial for identifying potential pavement distresses and developing effective pavement maintenance strategies. Here, a new Fuzzy C-Means-eXtreme Gradient Boosting (FCM-XGBoost) PCI prediction system is developed. First, asphalt pavement sections and concrete pavement sections are distinguished, and the Fuzzy C-Means (FCM) clustering algorithm is employed to cluster the asphalt and concrete expressway sections respectively based on multiple factors influencing PCI. Next, an eXtreme Gradient Boosting (XGBoost) model is trained for each cluster of expressway sections. Finally, in the system application stage, the PCI of an expressway section for the next year is predicted using the XGBoost model trained for the specific cluster that the expressway section belongs to. Validation results based on actual pavement condition data indicate that the proposed FCM-XGBoost PCI prediction system can achieve slightly higher accuracy compared with several high performance benchmark models, which include the Random Forest model, the Gradient Boosting Decision Tree model, the Light Gradient Boosting Machine model and the XGBoost model. The developed PCI prediction system contributes to the improvement of PCI prediction approaches and can be potentially applied in deploying effective and cost-efficient pavement maintenance strategies.

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

Accurately simulating glacier mass balance (GMB) data is crucial for assessing the impacts of climate change on glacier dynamics. Since physical models often face challenges in comprehensively accounting for factors influencing glacial melt and uncertainties in inputs, machine learning (ML) offers a viable alternative due to its robust flexibility and nonlinear fitting capability. However, the effectiveness of ML in modeling GMB data across diverse glacier types within High Mountain Asia has not yet been thoroughly explored. This study addresses this research gap by evaluating ML models used for the simulation of annual glacier-wide GMB data, with a specific focus on comparing maritime glaciers in the Niyang River basin and continental glaciers in the Manas River basin. For this purpose, meteorological predictive factors derived from monthly ERA5-Land datasets, and topographical predictive factors obtained from the Randolph Glacier Inventory, along with target GMB data rooted in geodetic mass balance observations, were employed to drive four selective ML models: the random forest model, the gradient boosting decision tree (GBDT) model, the deep neural network model, and the ordinary least-square linear regression model. The results highlighted that ML models generally exhibit superior performance in the simulation of GMB data for continental glaciers compared to maritime ones. Moreover, among the four ML models, the GBDT model was found to consistently exhibit superior performance with coefficient of determination (R2) values of 0.72 and 0.67 and root mean squared error (RMSE) values of 0.21 m w.e. and 0.30 m w.e. for glaciers within Manas and Niyang river basins, respectively. Furthermore, this study reveals that topographical and climatic factors differentially influence GMB simulations in maritime and continental glaciers, providing key insights into glacier dynamics in response to climate change. In summary, ML, particularly the GBDT model, demonstrates significant potential in GMB simulation. Moreover, the application of ML can enhance the accuracy of GMB modeling, providing a promising approach to assess the impacts of climate change on glacier dynamics.

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

AbstractAmong natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Nonlinear and Synergistic Effects of Built Environment Indicators on Street Vitality: A Case Study of Humid and Hot Urban Cities

Street vitality has become an important indicator for evaluating the attractiveness and potential for the sustainable development of urban neighborhoods. However, research on this topic may overestimate or underestimate the effects of different influencing factors, as most studies overlook the prevalent nonlinear and synergistic effects. This study takes the central urban districts of humid–hot cities in developing countries as an example, utilizing readily available big data sources such as Baidu Heat Map data, Baidu Map data, Baidu Building data, urban road network data, and Amap’s Point of Interest (POI) data to construct a Gradient-Boosting Decision Tree (GBDT) model. This model reveals the nonlinear and synergistic effects of different built environment factors on street vitality. The study finds that (1) construction intensity plays a crucial role in the early stages of urban street development (with a contribution value of 0.71), and as the city matures, the role of diversity gradually becomes apparent (with the contribution value increasing from 0.03 to 0.08); (2) the built environment factors have nonlinear impacts on street vitality; for example, POI density has different thresholds in the three cities (300, 200, and 500); (3) there are significant synergistic effects between different dimensions and indicators of the built environment, such as when the POI density is high and integration exceeds 1.5, a positive synergistic effect is notable, whereas a negative synergistic effect occurs when POI is low. This article further discusses the practical implications of the research findings, providing nuanced and targeted policy suggestions for humid–hot cities at different stages of development.

Prediction of Diabetes Using Data Mining and Machine Learning Algorithms: A Cross-Sectional Study.

This study aimed to develop a model to predict fasting blood glucose status using machine learning and data mining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life. This crosssectional study analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iran who participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minority over-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley values were calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numerical features to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost, random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy, sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting blood glucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overall with 0.737 area under the curve (AUC). A gradient boosted decision tree model accurately identified the most important risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.

Predicting Risk of Bullying Victimization among Primary and Secondary School Students: Based on a Machine Learning Model.

School bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machine learning approach (i.e., the gradient boosting decision tree model, GBDT), the present longitudinal study aims to systematically examine individual, family, and school environment factors that can predict the risk of bullying victimization among primary and secondary school students a year later. A total of 2767 participants (2065 secondary school students, 702 primary school students, 55.20% female students, mean age at T1 was 12.22) completed measures of 24 predictors at the first wave, including individual factors (e.g., self-control, gender, grade), family factors (family cohesion, parental control, parenting style), peer factor (peer relationship), and school factors (teacher-student relationship, learning capacity). A year later (i.e., T2), they completed the Olweus Bullying Questionnaire. The GBDT model predicted whether primary and secondary school students would be exposed to school bullying after one year by training a series of base learners and outputting the importance ranking of predictors. The GBDT model performed well. The GBDT model yielded the top 6 predictors: teacher-student relationship, peer relationship, family cohesion, negative affect, anxiety, and denying parenting style. The protective factors (i.e., teacher-student relationship, peer relationship, and family cohesion) and risk factors (i.e., negative affect, anxiety, and denying parenting style) associated with the risk of bullying victimization a year later among primary and secondary school students are identified by using a machine learning approach. The GBDT model can be used as a tool to predict the future risk of bullying victimization for children and adolescents and to help improve the effectiveness of school bullying interventions.

Hardware-Trojan Detection at Gate-Level Netlists Using a Gradient Boosting Decision Tree Model and Its Extension Using Trojan Probability Propagation

Hardware-Trojan Detection at Gate-Level Netlists Using a Gradient Boosting Decision Tree Model and Its Extension Using Trojan Probability Propagation

Prediction of central lymph node metastasis in patients with papillary thyroid microcarcinoma by gradient-boosting decision tree model based on ultrasound radiomics and clinical features.

In recent years, the study of radiomics in thyroid diseases has developed rapidly. This study aimed to establish a preoperative radiomics prediction model for central compartment lymph node metastases (CLNMs) in papillary thyroid microcarcinoma (PTMC) patients using gradient-boosting decision tree (GBDT) model and evaluate the performance of the model. A total of 274 patients with PTMC admitted for thyroid surgery at China-Japan Union Hospital of Jilin University from January 2020 to July 2022 were retrospectively analyzed. Patients were randomized into training and validation cohorts according to a ratio of 8:2. Radiomics features were extracted from the ultrasound (US) images of PTMC lesions. The open-source software Pyradiomics was used to extract radiomics features, and WEKA software was used to select CLNM-related radiomics features. Clinical risk factors for CLNM were screened by statistical methods. The GBDT model was constructed by combining radiomics features and clinical risk factors, and compared with the diagnostic efficacy of US-reported cervical lymph node status. Shapley Additive exPlanations (SHAP) was applied to visualize and analyze the GBDT model globally and locally. A total of seven radiomics features were significantly correlated with central lymph node status in the training and validation cohorts. The predictors in the GBDT model included the radiomics features, sex, age, and body mass index (BMI). The area under the curve (AUC) values of the GBDT model in the training and validation cohorts were 0.946 [95% confidence interval (CI): 0.920-0.972] and 0.845 (95% CI: 0.714-0.976), respectively, compared with 0.583 (95% CI: 0.508-0.659) and 0.582 (95% CI: 0.430-0.736) for US-reported lymph node status alone. The Delong test showed a significant difference between AUS in the training and validation cohorts (P<0.001, respectively). SHAP visual analysis showed the effect of each parameter on the GBDT model globally and locally. Decision curve analysis demonstrated the clinical utility of the GBDT model. The prediction of CLNM by the GBDT model, based on US radiomics features and clinical factors, can be better than that by using US alone in patients with PTMC. Furthermore, the GBDT model may serve a guidance of clinical decision for patient's treatment strategy.

Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature

In this study, extensive efforts have been devoted to optimizing the energy distribution and performance of a battery electric vehicle (BEV). An integrated simulation model based on energy flow test data is built and validated, and a parallel framework to implement automatic batch simulations is developed. On this basis, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) models of the BEV are established and compared, and many-objective optimization is carried out based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) algorithm. The results indicate that the developed parallel framework can efficiently perform automatic batch computations of the integrated simulation model for the BEV, and the CatBoost model demonstrates superior prediction performance on both the train and test sets. A uniformly distributed non-dominated set approximating the Pareto Front (PF) is obtained by the many-objective optimization, with the fourth non-dominated solution exhibiting a favorable optimization effect. The electricity consumption per 100 km, half-axis effective work, electricity recovered by battery, energy utilization and recovery efficiency are improved by 9.4 %, 12.2 %, 6.4 %, 96.3 % and 16.0 %, respectively. These findings can provide the theoretical basis, directional guidance and data support for the accurate modeling, batch simulation and many-objective optimization of BEVs.

Predicting and analyzing the fill factor of non-fullerene organic solar cells based on material properties and interpretable machine-learning strategies

In recent years, encouraging progress in non-fullerene acceptors-based organic solar cells (NFAs-OSCs) has been made in the field of clean-energy technologies. Nevertheless, achieving a high fill factor (FF) for NFAs-OSCs is a great challenge due to the FF value can be dramatically affected by various factors. An accurate prediction (based on empirical data) of how inherent characteristics of polymer-NFA combinations (e.g., frontier molecular orbitals (FMO) and charge-carrier mobilities) affects the FF in NFAs-OSCs is therefore urgently needed for the future commercialization prospect. The presented work demonstrates an outperforming predictive model using the optimized Gradient-boosting decision tree (GBDT) machine learning (ML) algorithm to accurately predict the FF value, which is based on a dataset consisting of > 180 unique donor/NFA blends with reported FF from previously published publications and experimental physical descriptors (FMO and charge-carrier mobilities). In addition, the SHapley Additive exPlanations (SHAP) strategy is further applied to not only gain the interpretability of the GBDT model prediction but also directly map the relationship between the FF value and experimental physical descriptors by systematic interpretable explanations (e.g., global and local). The SHAP analysis qualitatively reveals that the highest occupied molecular orbital (HOMO) of the donor and the HOMO of the acceptor are the two most important features associated with FF value prediction, which is in good agreement with previous experimental facts. Furthermore, the ML strategies explore the key requirements (e.g., small HOMO offset (<0.3 eV), high hole and electron mobilities (>1.0 × 10–4 cm2 V−1 s−1), and desired μh/μe ratio (in the range of 1.8–3.3)) for enhancing the FF values of NFAs-OSCs. This study provides novel insights into the possible physical mechanisms of FF enhancement, thus facilitating research efforts (e.g., rational design of energy level tuning in donor/NFA blend) for high-performance NFAs-OSCs.

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital.

Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission. First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories. The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model. Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

A fast and non-invasive artificial intelligence olfactory-like system that aids diagnosis of Parkinson's disease.

Several previous studies have shown that skin sebum analysis can be used to diagnose Parkinson's disease (PD). The aim of this study was to develop a portable artificial intelligence olfactory-like (AIO) system based on gas chromatographic analysis of the volatile organic compounds (VOCs) in patient sebum and explore its application value in the diagnosis of PD. The skin VOCs from 121 PD patients and 129 healthy controls were analyzed using the AIO system and three classic machine learning models were established, including the gradient boosting decision tree (GBDT), random forest and extreme gradient boosting, to assist the diagnosis of PD and predict its severity. A 20-s time series of AIO system data were collected from each participant. The VOC peaks at a large number of time points roughly concentrated around 5-12 s were significantly higher in PD subjects. The gradient boosting decision tree model showed the best ability to differentiate PD from healthy controls, yielding a sensitivity of 83.33% and a specificity of 84.00%. However, the system failed to predict PD progression scored by Hoehn-Yahr stage. This study provides a fast, low-cost and non-invasive method to distinguish PD patients from healthy controls. Furthermore, our study also indicates abnormal sebaceous gland secretion in PD patients, providing new evidence for exploring the pathogenesis of PD.

The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market.

Inattention of economic policymakers to default risk and making inappropriate decisions related to this risk in the banking system and financial institutions can have many economic, political and social consequences. In this research, it has been tried to calculate the default risk of companies listed in the capital market of Iran. To achieve this goal, two structural models of Merton and Geske, two machine learning models of Random Forest and Gradient Boosted Decision Tree, as well as financial information of companies listed in the Iranian capital market during the years 2016 to 2021 have been used. Another goal of this research is to measure the predictive power of the four models presented in the calculation of default risk. The results obtained from the calculation of the default rate of the investigated companies show that 50 companies listed in the Iranian capital market (46 different companies) have defaulted during the 5-year research period and are subject to the Bankruptcy Article of the Iranian Trade Law. Also, the results obtained from the ROC curves for the predictive power of the presented models show that the structural models of Merton and Geske have almost equal power, but the predictive power of the Random Forest model is a little more than the Gradient Boosted Decision Tree model.

Splitting tensile strength prediction of Metakaolin concrete using machine learning techniques

Splitting tensile strength (STS) is an important mechanical property of concrete. Modeling and predicting the STS of concrete containing Metakaolin is an important method for analyzing the mechanical properties. In this paper, four machine learning models, namely, Artificial Neural Network (ANN), support vector regression (SVR), random forest (RF), and Gradient Boosting Decision Tree (GBDT) were employed to predict the STS. The comprehensive comparison of predictive performance was conducted using evaluation metrics. The results indicate that, compared to other models, the GBDT model exhibits the best test performance with an R2 of 0.967, surpassing the values for ANN at 0.949, SVR at 0.963, and RF at 0.947. The other four error metrics are also the smallest among the models, with MSE = 0.041, RMSE = 0.204, MAE = 0.146, and MAPE = 4.856%. This model can serve as a prediction tool for STS in concrete containing Metakaolin, assisting or partially replacing laboratory compression tests, thereby saving costs and time. Moreover, the feature importance of input variables was investigated.

Abstract 17213: Machine Learning-Based Prediction of Type A Aortic Dissection

Background: Existing risk predictors of aortic dissection have certain limitations. We hypothesized that machine learning models trained on clinical, demographic, and anthropometric features can further improve the prediction of patient outcomes. Objective: This study aims to develop a machine learning model that predicts type A aortic dissection and can help clinical decision making. Methods: This cohort study used the Yale Aortic Institute database. The models incorporated variables spanning demographic, anthropometric, medical history, radiological, and laboratory domains. The models were trained and validated using stratified 10-fold cross-validation. Hyperparameters for each algorithm were tuned through grid-search on the training folds. The models were trained to optimize the area under the receiver operator characteristic curve (AUROC) and were assessed in a held-out test set. Results: A total of 2,109 patients were analyzed in our study. Among them, 271 were diagnosed with type A aortic dissection. The models demonstrated strong performance on the held-out test set. Specifically, the extreme gradient boosting decision tree model achieved an AUROC of 0.821, while the random forest model achieved an AUROC of 0.820. Importantly, these models outperformed the prediction of type A aortic dissection when based solely on the ascending aorta diameter, which had an AUROC of 0.549. Besides the ascending aorta diameter, the key predictors were age, weight, height, family history, smoking, bicuspid aortic valve, and hypertension. Conclusion: We developed a machine learning model that provides an individualized prediction of the development of type A aortic dissection. This approach provides an accessible, efficient, and remote tool to identify high-risk patients.