Classical Logistic Regression Model Research Articles

Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights

BackgroundA dearth of studies showed that infectious diseases cause the majority of deaths among under-five children. Worldwide, Acute Respiratory Infection (ARI) continues to be the second most frequent cause of illness and mortality among children under the age of five. The paramount disease burden in developing nations, including Ethiopia, is still ARI.ObjectiveThis study aims to determine the magnitude and predictors of ARI among under-five children in Ethiopia using used state of the art machine learning algorithms.MethodsData for this study were derived from the 2016 Ethiopian Demographic and Health Survey. To predict the determinants of acute respiratory infections, we performed several experiments on ten machine learning algorithms (random forests, decision trees, support vector machines, Naïve Bayes, and K-nearest neighbors, Lasso regression, GBoost, XGboost), including one classic logistic regression model and an ensemble of the best performing models. The prediction ability of each machine-learning model was assessed using receiver operating characteristic curves, precision-recall curves, and classification metrics.ResultsThe total ARI prevalence rate among 9501 under-five children in Ethiopia was 7.2%, according to the findings of the study. The overall performance of the ensemble model of SVM, GBoost, and XGBoost showed an improved performance in classifying ARI cases with an accuracy of 86%, a sensitivity of 84.6%, and an AUC-ROC of 0.87. The highest performing predictive model (the ensemble model) showed that the child’s age, history of diarrhea, wealth index, type of toilet, mother’s educational level, number of living children, mother’s occupation, and type of fuel they used were an important predicting factor for acute respiratory infection among under-five children.ConclusionThe intricate web of factors contributing to ARI among under-five children was identified using an advanced machine learning algorithm. The child’s age, history of diarrhea, wealth index, and type of toilet were among the top factors identified using the ensemble model that registered a performance of 86% accuracy. This study stands as a testament to the potential of advanced data-driven methodologies in unraveling the complexities of ARI in low-income settings.

GPT classifications, with application to credit lending

Generative Pre-trained Transformers (GPT) and Large language models (LLMs) have made significant advancements in natural language processing in recent years. The practical applications of LLMs are undeniable, rendering moot any debate about their impending influence. The power of LLMs has made them similar to machine learning models for decision-making problems. In this paper, we focus on binary classification which is a common use of ML models, particularly in credit lending applications. We show how a GPT model can perform almost as accurately as a classical logistic machine learning model but with a much lower number of sample observations. In particular, we show how, in the context of credit lending, LLMs can be improved and reach performances similar to classical logistic regression models using only a small set of examples.

Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Logistic regression versus XGBoost for detecting burned areas using satellite images

Classical statistical methods prove advantageous for small datasets, whereas machine learning algorithms can excel with larger datasets. Our paper challenges this conventional wisdom by addressing a highly significant problem: the identification of burned areas through satellite imagery, that is a clear example of imbalanced data. The methods are illustrated in the North-Central Portugal and the North-West of Spain in October 2017 within a multi-temporal setting of satellite imagery. Daily satellite images are taken from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Our analysis shows that a classical Logistic regression (LR) model competes on par, if not surpasses, a widely employed machine learning algorithm called the extreme gradient boosting algorithm (XGBoost) within this particular domain.

What are the root causes of material delivery schedule inaccuracy in supply chains?

PurposeThis study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?Design/methodology/approachA mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.FindingsThe effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.Practical implicationsThe findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.Originality/valueThe findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Autism Spectrum Disorder Prediction in Children Using Machine Learning

Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.

Analysis and research on the chemical composition of ancient Chinese glass

The purpose of this study is to investigate the classification rules for lead-barium and highpotassium glass. First, we noticed that the glass types differed considerably for the five components. After analyzing the data and establishing the independent T-test model, We developed the Logistic regression classification model using these components as independent factors and the glass types as dependent variables, and we derived the classification function for the glass types. Second, high-potassium glass, was divided into two subclasses based on calcium and aluminum content using the K-means++ model and associated literature on the chemical composition of ancient Chinese glass [1][2]. These were grass-ash high-potassium glass and nitrate high-potassium glass, as well as two types of lead-barium glass: lead-sodium glass and lead-calcium glass. Finally, the cluster center coordinate values were perturbed by -10% and 10%, verifying the classification of each sample under different perturbations, and the resulting subclass divisions remained unchanged, confirming the low sensitivity of the classification results.

Inflammation indexes and machine-learning algorithm in predicting urethroplasty success.

To assess the predictive capability of hematological inflammatory markers for urethral stricture recurrence after primary urethroplasty and to compare traditional statistical methods with a machine-learning-based artificial intelligence algorithm. Two hundred eighty-seven patients who underwent primary urethroplasty were scanned. Ages, smoking status, comorbidities, hematological inflammatory parameters (neutrophil-lymphocyte ratios, platelet-lymphocyte ratios [PLR], systemic immune-inflammation indexes [SII], and pan-immune-inflammation values [PIV]), stricture characteristics, history of previous direct-visual internal urethrotomy, urethroplasty techniques, and grafts/flaps placements were collected. Patients were followed up for one year for recurrence and grouped accordingly. Univariate and multivariate logistic regression analyses were conducted to create a predictive model. Additionally, a machine-learning-based logistic regression analysis was implemented to compare predictive performances. p<0.05 was considered statistically significant. Comparative analysis between the groups revealed statistically significant differences in stricture length (p=0.003), localization (p=0.027), lymphocyte counts (p=0.008), PLR (p=0.003), SII (p=0.003), and PIV (p=0.001). In multivariate analysis, stricture length (odds ratio [OR] 1.230, 95% confidence interval [CI] 1.142-1.539, p<0.0001) and PIV (OR 1.002, 95% CI 1.000-1.003, p=0.039) were identified as significant predictors of recurrence. Classical logistic regression model exhibited a sensitivity of 0.76, specificity of 0.43 with an area under curve (AUC) of 0.65. However, the machine-learning algorithm outperformed traditional methods achieving a sensitivity of 0.80, specificity of 0.76 with a higher AUC of 0.82. PIV and machine-learning algorithms shows promise on predicting urethroplasty outcomes, potentially leading to develop possible nomograms. Evolving machine-learning algorithms will contribute to more personalized and accurate approaches in managing urethral stricture.

A Comparative Study of Machine Learning Models For NCAA Men’s Basketball Tournament Games Outcome Prediction

The evolution of machine learning has produced many avant-garde prediction algorithms, some of which provide more accurate predictions than conventional statistical tools. For the last two decades, machine learning (ML) approaches have largely been employed to predict game results. The purpose of this study is to utilize data gathered from web sources on the performance of 354 NCAA basketball teams over five seasons from 2015 to 2019 to forecast the results of NCAA men’s basketball tournament games with an assortment of big data and machine learning classification models. Among these include Sweet Sixteen, Elite Eight, and Final Four. The prediction results of each model were analysed and compared, and Decision Tree showed the best prediction performance compared with KNN, Logistic Regression, and SVM classification models, with a prediction accuracy of 75.71%, but Decision Tree was prone to overfitting problems. However, the Decision Tree is prone to overfitting problems, while Random Forest can correct the overfitting problem of Decision Tree by bagging and reduce the variance of Decision Tree prediction. Therefore, this study hypothesized that Random Forest would outperform Decision Tree in predicting NCAA game results. The results showed that, after a comprehensiveanalysis and comparison of the evaluation metrics of the Decision Tree and Random Forest models, Random Forest was found to have better acceptable forecast performance than Decision Tree, with a prediction accuracy of 85.71%.

Design of cultural and creative experience products in scenic spots based on CORE model

Abstract Under the background of “cultural and tourism integration”, scenic cultural and creative experience products are an important means to enhance the communication power of scenic spots and generate income. In this paper, the CORE model is used as the design concept, and the CORE design process for scenic cultural and creative products is constructed. The plain Bayesian classification algorithm is used to extract review feedback from the product, and the vectorization of the extracted reviews is achieved using the FastText model. The vectorized reviews are input into the logistic regression classification model to obtain sentiment classification results. A fused sentiment feature vector is constructed by combining Apirior and a sentiment dictionary aimed at separating sentiment vector classification. Finally, the experience effect of cultural and creative products is explored as an example of X scenic spot, and the optimization strategy of product design is explored. The attribute feature of X Scenic Spot’s cultural and creative experience product, “color” embraces four emotional attributes, and the corresponding adaptations are 4.77, 5.42, 5.40, and 5.38, respectively. The SDs of the emotional attributes of the existing product are The SDs of the emotional attributes of the existing products are 1.141, 1.025, and 0.184, respectively, and the differences between the product descriptions and the users’ opinions on the perceptual attributes are large, which provides the evaluation correlation in the CORE model. Under the guidance of the CORE model, four aspects of cultural and creative marketing, scenic area IP, consumer needs and product experience dimensions are proposed.

Machine learning based classification of yogurt aroma types with flavoromics

Traditional sensory evaluation, relying on human assessors, is vulnerable to subjective error and lacks automation. Nonetheless, the complexity of human sensation makes it challenging to develop a computational method in place of human sensory evaluation. To tackle this challenge, this study constructed logistic regression classification models that could predict yogurt aroma types based on aroma-active compound concentrations with high classification accuracy (AUC ROC > 0.8). Furthermore, indicator compounds discovered from feature importance analysis of classification models led to the derivation of classification criteria of yogurt aroma types. Through constructing and analyzing machine learning models on yogurt aroma types, this study provides an automated pipeline to monitor sensory properties of yogurts.

Utilisation of artificial intelligence to predict early readmission to the cardiac intensive care unit in critically ill patients

Abstract Background The need for cardiac intensive care unit (ICU) beds remains high in order to treat patients with severe cardiovascular diseases. Therefore, safe and timely discharge policies from the cardiac ICU to normal wards are crucial to meet the continuously increasing demand for cardiac ICU beds. ICU readmission after ICU discharge has been shown to increase mortality, hospital length of stay and costs. Artificial intelligence may provide a timely and accurate discharge decision support system for intensivists based on comprehensive, multi-dimensional ICU data. Purpose This is the first study to develop and validate a neuronal network, called Feedforward Neural Network (FNN), to safely discharge patients from the cardiac ICU and predict ICU readmission within 72 hours. Methods Adult patients who were admitted to the cardiac ICU due to a primary cardio-pulmonary admission diagnosis (such as acute myocardial infarction, cardiogenic shock or cardiac arrest) at a German tertiary care center between 2003 and 2021 were included. The dataset was split into a training and test dataset. The FNN was trained with 127 variables, including patient demographics, co-morbidities, medical diagnoses, disease acuity levels, laboratory values, vital parameters, ICU procedures and administered ICU medications. FNN was compared to the predictability of a classic logistic regression model and the Stability and Workload Index for Transfer (SWIFT) score, which is a clinical score to predict ICU readmission. Results 2,498 adult patients in the cardiac ICU met the inclusion criteria, out of which the FNN was trained in 1,998 (80.0%) patients and tested in 500 (20.0%) patients. The FNN had a 97.9% sensitivity, 86.9% specificity, 99.4% precision and 97.4% accuracy. FNN showed a better predictability of early ICU readmission than the logistic regression model and the SWIFT score: areas under the receiver operating characteristic (AUC ROC) curves were 90.78% vs. 71.34% vs. 71.13% (P &amp;lt;0.001) (Figure), respectively. Conclusions ICU transfer to the normal ward is considered to be a high-risk event and the vast amount of daily clinical, physiologic, laboratory and monitoring data may overwhelm critical care providers in making well informed decisions on patient discharge readiness. Artificial intelligence might be used as a sensitive, specific and accurate decision support system to help with patient discharge management and reduce ICU readmission rates.

A new ordinal mixed-data sampling model with an application to corporate credit rating levels

In this paper we propose a new ordinal logistic regression model (OLMIDAS) that allows the inclusion of independent variables at higher frequencies than that of the dependent variable. A simulation study shows that our proposed model can find the true patterns in the data. In an empirical study we apply OLMIDAS to the prediction of corporate credit rating levels and compare its performance to classical logistic regression models with an annual aggregation of the higher-frequency variable, such as ordinal logistic regression and multinomial logistic regression. We find that OLMIDAS outperforms the classical logistic regression models while providing additional knowledge of the structure of the higher-frequency explanatory variable.

Rapid prediction of the aroma type of plain yogurts via electronic nose combined with machine learning approaches

The unique aroma attributes of yogurts are associated with their quality and consumer acceptance. However, the aroma types of plain yogurts and methods for quickly identifying them have not been systematically established. This study used quantitative descriptive analysis (QDA) to label the aromas of commercial plain yogurts into four primary types, namely fermented, cheesy, milky, and fruity, for the first time. Electronic nose (e-nose) data were rapidly collected from the headspace of plain yogurt samples, and the aroma types classification models were established using linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and decision trees (DT). Among these models, the SVM-based model displayed the best classification performance, with accuracy and F1 values at 0.933. Furthermore, this study revealed that the LR classification model, based on refined smaller-size e-nose datasets, improved the accuracy and F1 values to 0.867 and 0.872, respectively, compared to the original LR model using the complete dataset, thus demonstrating the applicability of model simplification. This work provides a theoretical basis for rapid plain yogurt aroma type identification by combining e-nose data with machine learning approaches.

Prediction Models of Blood Glucose Change During Aerobic Exercise Using Machine Learning Techniques

PURPOSE: This study aimed to explore the relationship between blood glucose level changes and body characteristics during exercise and to present six models for predicting changes in blood glucose levels during exercise.METHODS: 148 healthy men and women (age: 31.9±9.7 year, fasting blood glucose: 102.1±14.1 mg/dL, &lt;i&gt;p&lt;/i&gt;=.032) participated in the study, and 30 of them participated in the study. Eight variables were selected to build two prediction models: 24-hour ingested carbohydrates, age, blood glucose, heart rate changes, sex, skeletal muscle mass, heart rate recovery after exercise, and resting heart rate. Logistic regression and random forest classifier models were used to predict the changes in blood glucose levels during exercise.RESULTS: A total of six models were created for all participants, male and female. Random forest classification (training set: AUC=0.837, Youden index=0.66; validation set: AUC=0.730, Youden index=0.53) and logistic regression classification models (training set: AUC=0.807, Youden index=0.55; validation set: AUC=0.735, Youden index=0.57) were built.CONCLUSION: The random forest model showed good performance in classifying internal data, whereas the logistic regression classification model demonstrated relatively good performance in classifying validation data.

Performance of Radiomics Based Multi-Parameter Models Derived from Lung CT in Differentiating Benign Non-Inflammatory, Inflammatory and Malignant Pulmonary Nodule

Pulmonary nodules are a significant clinical issue that require accurate and efficient diagnosis. This study constructed a machine learning model, combining radiomics features of chest CT sequences with three types of microvascular density (MVD) values, to differentiate benign, inflammatory and malignant pulmonary nodules. A total of 100 patients with lung nodules on CT images and corresponding pathological results were retrospectively included in the study. The MVD values and radiomics features were calculated and extracted based on the segmented nodules. Univariate correlation analysis and principal component analysis were performed to select radiomics features. Combined MVD values and selected radiomics features, we conducted a logistic regression classification model. The area under the curve (AUC) was applied to show model performance. Our model reached an AUC value of 0.867 when tested on independent datasets. The performance of the model to differentiate benign, malignant and inflammatory nodules reached AUC values of only 0.908, 0.833, and 0.730, respectively. We conducted a prediction model that shows promising results in distinguishing three different types of lung nodules.

Anomaly prediction of CT equipment based on IoMT data

BackgroundLarge-scale medical equipment, which is extensively implemented in medical services, is of vital importance for diagnosis but vulnerable to various anomalies and failures. Most hospitals that conduct regular maintenance have been suffering from medical equipment-related incidents for years. Currently, the Internet of Medical Things (IoMT) has emerged as a crucial tool in monitoring the real-time status of the medical equipment. In this paper, we develop an IoMT system of Computed Tomography (CT) equipment in the West China Hospital, Sichuan University and collected the system status time-series data. Novel multivariate time-series classification models and frameworks are proposed to predict the anomalies of CT equipment. The important features that are closely related to the equipment anomalies are identified with the model.MethodsWe extracted the real-time CT equipment status time-series data of 11 equipment between May 19, 2020 and May 19, 2021 from the IoMT, which includes the equipment oil temperature, anode voltage, etc. The arcs are identified as labels of anomalies due to their relationship with decreased imaging quality and CT equipment failures. To improve prediction accuracy, the statistics and transformations of the raw historical time-series data segment in the sliding time window are used to construct new features. Due to the particularity of time-series data, two frameworks are proposed for splitting the training and test sets. Then the Decision Tree, Support Vector Machine, Logistic Regression, Naive Bayesian, and K-Nearest Neighbor classification models are used to classify the system status. We also compare our model to state-of-the-art models.ResultsThe results show that the anomaly prediction accuracy and recall of our method are 79% and 77%, respectively. The oil temperature and anode voltage are identified as the decisive features that may lead to anomalies. The proposed model outperforms the others when predicting the anomalies of the CT equipment based on our dataset.ConclusionsThe proposed method could predict the state of CT equipment and be used as a reference for practical maintenance, where unexpected anomalies of medical equipment could be reduced. It also brings new insights into how to handle non-uniform and imbalanced time series data in practical cases.

UPLC-QDA and machine learning for distinguishing different commodity specifications of Fritillariae Cirrhosae Bulbus and application of data augmentation technology

This study aimed to establish a method based on machine learning technology for accurately predicting the commodity specifications of Fritillariae Cirrhosae Bulbus and explore the application of data augmentation technology in the field of drug analysis. The correlation optimized warping(COW) algorithm was used to perform peak calibration on the UPLC-QDA multi-channel superimposed data of 30 batches of samples, and the data were normalized. Through unsupervised learning methods such as clustering analysis, principal component analysis(PCA), and correlation analysis, the general characteristics of the data were understood. Then, the logistic regression algorithm was used for supervised learning on the data, and the condition tabular generative adversarial networks(CTGAN) was used to generate a large amount of data. Logistic regression classification models were trained separately using the real data and the data generated by CTGAN, and these models were evaluated. The logistic regression model trained with real data achieved cross-validation and test set accuracies of 0.95 and 1.00, respectively, while the logistic regression model trained with both real and CTGAN-generated data achieved cross-validation and test set accuracies of 0.99 and 1.00, respectively. The results indicate that machine learning can accurately predict the classification of Songbei, Qingbei, and Lubeibased on UPLC-QDA detection data. CTGAN-generated data can partially compensate for the lack of data in drug analysis, improving the accuracy and predictive ability of machine learning models.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study.

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors. A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models. During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors. Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

Establishment risk of invasive golden mussel in a water diversion project: An assessment framework

Inter-basin water diversion projects have led to accelerated colonization of aquatic organisms, including the freshwater golden mussel (Limnoperna fortunei), exacerbating global biofouling concerns. While the influence of environmental factors on the mussel's invasion and biofouling impact has been studied, quantitative correlations and underlying mechanisms remain unclear, particularly in large-scale inter-basin water diversion projects with diverse hydrodynamic and environmental conditions. Here, we examine the comprehensive impact of environmental variables on the establishment risk of the golden mussel in China's 1432-km-long Middle Route of the South-to-North Water Diversion Project. Logistic regression and multiclass classification models were used to investigate the environmental influence on the occurrence probability and reproductive density of the golden mussel. Total nitrogen, ammonia nitrogen, water temperature, pH, and velocity were identified as crucial environmental variables affecting the biofouling risk in the project. Logistic regression analysis revealed a negative correlation between the occurrence probability of all larval stages and levels of total nitrogen and ammonia nitrogen. The multiclass classification model showed that elevated levels of total nitrogen hindered mussel reproduction, while optimal water temperature enhanced their reproductive capacity. Appropriate velocity and pH levels were crucial in maintaining moderate larval density. This research presents a quantitative analytical framework for assessing establishment risks associated with invasive mussels, and the framework is expected to enhance invasion management and mitigate biofouling issues in water diversion projects worldwide.