Construction of recognition models for subthreshold depression based on multiple machine learning algorithms and vocal emotional characteristics

Breast cancer is one of the illnesses that has become a leading cause of female death worldwide, and early diagnosis of these diseases is a difficult and important undertaking. The intention of this paper is to design prediction models based on Decision Tree, Random Forest and Linear Discriminant Analysis respectively to forecast the breast cancer occurrence at an early stage. The design is focused on analyzing the minimum set of attributes from the clinical data set that have been chosen by experts, and then compare the performances of the models to find their advantages in each model. Breast Cancer Wisconsin Data Set is used in this paper to construct the expected prediction models. Classification accuracy which is determined by comparing real values with predicted values, and generalization ability which is reflected by Receiver Operating Characteristic curve, are used to gauge the performance of the models. The results confirm that the Random Forest model can achieve the highest accuracy to 99.4% and the best generalization ability. However, the Linear Discriminant Analysis model can keep better stability in prediction accuracy and has the fastest running speed.

The tree crown is an important part of a tree and is closely related to forest growth status, forest canopy density, and other forest growth indicators. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is an important tree species in southern China. A three-dimensional (3D) visualization assistant decision-making system of plantations could be improved through the construction of crown contour envelope models (CCEMs), which could aid plantation production. The goal of this study was to establish CCEMs, based on random forest and mathematical modeling, and to compare them. First, the regression equation of a tree crown was calculated using the least squares method. Then, forest characteristic factors were screened using methods based on mutual information, recursive feature elimination, least absolute shrink and selection operator, and random forest, and the random forest model was established based on the different screening results. The accuracy of the random forest model was higher than that of the mathematical modeling. The best performing model based on mathematical modeling was the quartic polynomial with the largest crown radius as the variable (R-squared (R2) = 0.8614 and root mean square error (RMSE) = 0.2657). Among the random forest regression models, the regression model constructed using mutual information as the feature screening method was the most accurate (R2 = 0.886, RMSE = 0.2406), which was two percentage points higher than mathematical modeling. Compared with mathematical modeling, the random forest model can reflect the differences among trees and aid 3D visualization of a Chinese fir plantation.

Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high-risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real-world database containing clinical features and employing numerous artificial intelligent approach algorithms. This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid-modifying drug use. This study successfully developed a highly accurate 4-year risk model for pancreatic cancer in patients with diabetes using real-world clinical data and multiple machine-learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Introduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.

To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Cardiovascular disease is the leading cause of mortality among nonalcoholic steatohepatitis (NASH) patients who undergo liver transplants. In the present study, machine learning algorithms were used to identify important risk factors for cardiovascular death and to develop a prediction model. The Standard Transplant Analysis and Research data were gathered from the Organ Procurement and Transplantation Network. After cleaning and preprocessing, the dataset comprised 10,871 patients and 92 features. Recursive feature elimination (RFE) and select from model (SFM) were applied to select relevant features from the dataset and avoid overfitting. Multiple machine learning algorithms, including logistic regression, random forest, decision tree, and XGBoost, were used with RFE and SFM. Additionally, prediction models were developed using a support vector machine, Gaussian naïve Bayes, K-nearest neighbors, random forest, and XGBoost algorithms. Finally, SHapley Additive exPlanations (SHAP) were used to increase interpretability. The findings showed that the best feature selection method was RFE with a random forest estimator, and the most critical features were recipient and donor blood type, body mass index, recipient and donor state of residence, serum creatinine, and year of transplantation. Furthermore, among all the outcomes, the XGBoost model had the highest performance, with an accuracy value of 0.6909 and an area under the curve value of 0.86. The findings also revealed a predictive relationship between features and cardiovascular death after liver transplant among NASH patients. These insights may assist clinical decision-makers in devising strategies to prevent cardiovascular complications in post-liver transplant NASH patients.

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2 ), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Land-use regression model and machine learning algorithms in assessing outdoor noise exposure in Hong KongBackground/Aim: There are epidemiological studies in reporting the association between environmental noise exposure and various health outcomes, including cardiovascular diseases and cognitive decline. However, evidence in using land-use regression (LUR) model in predicting noise is limited and not conclusive, while machine learning algorithms are seemed to be a novel method in improving the explanative power of noise prediction. In this study, random forest (RF) model was adopted to estimate outdoor noise level in Hong Kong and compare the performance between LUR and RF. Methods: A total of 102 measurement sites with 27 environment-related variables were used to estimate the spatial environmental noise variation. Two separate measurements for A-weighted equivalent sound pressure levels over 24 hours (Leq,24h) were conducted at each site during the 2019-2020 period. Land use types, traffic information and geographic information systems were chosen to the related variables for building models. Ordinary linear regression was adopted to be the traditional land-use regression model. Results: The annual mean of 102 measurement sites of Leq,24h was 66.3 dB(A). The proportion of variation (R2) explained by the random forest model in full-frequency noise was higher than the traditional LUR (R2: RF: 0.76, LUR: 0.69). Random forest performed also better in lower root mean squared errors (RF: 2.91 dB(A), LUR: 3.22 dB(A)). Further results on the noise level in different time periods and frequencies will be described in the conference. Conclusion: Random forest model is suggested to be a better alternative in estimating noise exposure. Further study on the application of the model on epidemiological studies can be conducted.

ObjectiveTo develop and validate a classification model for insulin resistance in female individuals who have survived breast cancer using easily obtainable clinical and demographic features.MethodsData were obtained from the U.S. National Health and Nutrition Examination Survey (NHANES) spanning 1999 to March 2020. A total of 340 female individuals who have survived breast cancer were included, and participants were randomly assigned to a training set (n = 239) and a testing set (n = 101). Multiple machine learning algorithms were trained, including Logistic Regression, Random Forest, and Support Vector Machine. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).ResultsAll models demonstrated strong classification performance in the testing set, with AUC values exceeding 0.87. Among them, the Random Forest and Support Vector Machine models showed superior performance in DCA. Of the seven input features—body mass index, fasting blood glucose, triglyceride, HDL cholesterol, poverty income ratio, race, and education—fasting blood glucose had the highest positive feature importance for classifying insulin resistance.ConclusionsThis study demonstrates the feasibility of using machine learning algorithms to accurately predict insulin resistance in individuals who have survived breast cancer with a limited set of clinical and demographic variables. The Random Forest and Support Vector Machine models, in particular, offer strong classification performance and may support clinicians in early identification and management of insulin resistance among individuals in this high-risk population.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03189-z.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital- and community-acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in-hospital mortality. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI-TOF MS-based methodology for MRSA screening with machine-learning algorithms. A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin-sensitive S. aureus (MSSA). The mass-to-charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non-linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model. A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non-linear RBF-SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF-SVM) and 0.82 (RF). Our study demonstrates that MALDI-TOF MS coupled with machine-learning algorithms could be used to develop a rapid and easy-to-use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost-effective and time-efficient alternative to conventional resistance detection in the future to improve clinical treatment.

Simple SummaryThe objective response rate of lenvatinib combined with transarterial chemoembolization for unresectable hepatocellular carcinoma is unsatisfactory. We aimed to develop predictive models using demographic characteristics, pre-treatment serum biomarkers and tumor characteristics of unresectable hepatocellular carcinoma patients by five machine learning algorithms to predict the response under combined treatments. We identified the 10 most important predictors, including K, low-density lipoprotein, D-dimer, red blood cell, alanine aminotransferase, albumin, monocyte, tumor size, triglyceride, and age. In addition, we applied the Shapley Additive exPlanation to explain the best-performing random forest predictive model to provide a reasonable explanation of the efficacy prediction at an individualized level. The combination of machine learning and Shapley Additive exPlanation can provide valuable suggestions for clinical decision making.Background: Lenvatinib and transarterial chemoembolization (TACE) are first-line treatments for unresectable hepatocellular carcinoma (HCC), but the objective response rate (ORR) is not satisfactory. We aimed to predict the response to lenvatinib combined with TACE before treatment for unresectable HCC using machine learning (ML) algorithms based on clinical data. Methods: Patients with unresectable HCC receiving the combination therapy of lenvatinib combined with TACE from two medical centers were retrospectively collected from January 2020 to December 2021. The response to the combination therapy was evaluated over the following 4–12 weeks. Five types of ML algorithms were applied to develop the predictive models, including classification and regression tree (CART), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The performance of the models was assessed by the receiver operating characteristic (ROC) curve and area under the receiver operating characteristic curve (AUC). The Shapley Additive exPlanation (SHAP) method was applied to explain the model. Results: A total of 125 unresectable HCC patients were included in the analysis after the inclusion and exclusion criteria, among which 42 (33.6%) patients showed progression disease (PD), 49 (39.2%) showed stable disease (SD), and 34 (27.2%) achieved partial response (PR). The nonresponse group (PD + SD) included 91 patients, while the response group (PR) included 34 patients. The top 40 most important features from all 64 clinical features were selected using the recursive feature elimination (RFE) algorithm to develop the predictive models. The predictive power was satisfactory, with AUCs of 0.74 to 0.91. The SVM model and RF model showed the highest accuracy (86.5%), and the RF model showed the largest AUC (0.91, 95% confidence interval (CI): 0.61–0.95). The SHAP summary plot and decision plot illustrated the impact of the top 40 features on the efficacy of the combination therapy, and the SHAP force plot successfully predicted the efficacy at the individualized level. Conclusions: A new predictive model based on clinical data was developed using ML algorithms, which showed favorable performance in predicting the response to lenvatinib combined with TACE for unresectable HCC. Combining ML with SHAP could provide an explicit explanation of the efficacy prediction.

Background/Objectives: Diabetic kidney disease (DKD) is a major cause of end-stage kidney disease and a leading contributor to morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). However, predictive models for DKD onset in Korean patients with T2DM remain underexplored. This study aimed to develop and validate a machine learning (ML)-based DKD prediction model for this population. Methods: This retrospective study utilized electronic health records from six secondary or tertiary hospitals in Korea. The Jeonbuk National University Hospital cohort was used for model development (ratio training: test data, 8:2), whereas datasets from five other hospitals supported external validation. We employed multiple ML algorithms, including lasso, ridge, and elastic net regression; random forest; XGBoost; support vector machines; and neural networks. The model incorporated demographic variables, comorbidities, medications, and laboratory test results. Results: Among 5120 patients with T2DM, 1361 (26.6%) developed DKD. In the development cohort, XGBoost achieved the highest predictive performance (AUC: 0.8099), followed by random forest and logistic regression models (AUCs: 0.7977-0.8019). External validation confirmed the model's robustness with high AUCs (XGBoost: 0.8113, logistic regression models: 0.8228-0.8271). Key predictive factors included age; baseline estimated glomerular filtration rate; and creatinine, hemoglobin, and hemoglobin A1c levels. Conclusions: Our findings highlight the potential of ML-based approaches in predicting DKD in patients with T2DM. The superior performance of XGBoost and logistic regression models underscores their clinical utility. External validation supports the model's generalizability. This model is a valuable tool for the early DKD risk assessment of Korean patients with T2DM.

This study comprehensively considers soil formation factors such as land use types, soil types, depths, and geographical conditions in Lanxi City, China. Using multi-source public data, three environmental variable screening methods, the Boruta algorithm, Recursive Feature Elimination (RFE), and Particle Swarm Optimization (PSO), were used to optimize and combine 47 environmental variables for the modeling of soil pH based on the data collected from farmland in the study area in 2022, and their effects were evaluated. A Random Forest (RF) model was used to predict soil pH in the study area. At the same time, Pearson correlation analysis, an environmental variable importance assessment based on the RF model, and SHAP explanatory model were used to explore the main controlling factors of soil pH and reveal its spatial differentiation mechanism. The results showed that in the presence of a large number of environmental variables, the model with covariates selected by PSO before the application of the Random Forest algorithm had higher prediction accuracy than that of Boruta–RF, RFE–RF, and all variable prediction RF models (MAE = 0.496, RMSE = 0.641, R2 = 0.413, LCCC = 0.508). This indicates that PSO, as a covariate selection method, effectively optimized the input variables for the RF model, enhancing its performance. In addition, the results of the Pearson correlation analysis, RF-model-based environmental variable importance assessment, and SHAP explanatory model consistently indicate that Channel Network Base Level (CNBL), Elevation (DEM), Temperature mean (T_m), Evaporation (E_m), Land surface temperature mean (LST_m), and Humidity mean (H_m) are key factors affecting the spatial differentiation of soil pH. In summary, the approach of using PSO for covariate selection before applying the RF model exhibits high prediction accuracy and can serve as an effective method for predicting the spatial distribution of soil pH, providing important references for accurately simulating the spatial mapping of soil attributes in hilly and basin areas.