GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life. This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data. The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.

Introduction: No accurate diagnostic tests exist for exocrine pancreatic insufficiency (EPI). As a result, EPI is misdiagnosed, including in chronic pancreatitis (CP), and there is an unmet need for clinical tools to help clinicians evaluate patients with CP at risk for EPI. The aim of this study was to generate a simple clinical tool to predict EPI in patients with CP. Methods: 49 variables from medical records of CP patients with or without EPI were entered into a Classification and Regression Tree (CART) model using the unimputed full analysis set and 4 other models (Logistic Regression with Least Absolute Shrinkage and Selection Operator regularization, Support Vector Machine, Random Forest, and Gradient Boosting Machine) using imputed full analysis set. EPI misclassification rate (mRate) served as primary metric to train the 5 prediction models. Each model’s generalizability was assessed using 5-fold cross-validation and the model that was best suited for clinician use based on mRate and other characteristics was selected. Results: Records of 274 patients with CP from 6 pancreatitis centers across the US were included. Among the 160 (58%)/114 (42%) patients with EPI/without EPI, 48%/55% were female, mean age was 55/52 years, 76%/69% were white, 29%/52% were never smokers, and 39%/53% were never alcohol users. All 5 prediction models demonstrated similar EPI mRates. The CART model was selected. The final CART decision tree includes 10 variables (in order of importance): smoking, pancreatic atrophy, steatorrhea, alcohol use, hemoglobin, serum vitamin D, pancreatic duct contour change, serum albumin, pancreatic parenchymal calcifications, and use of opioid analgesics. The mRate without and with 5-fold cross-validation of the CART was 0.153 (training error) and 0.314 (prediction error), and the area under the receiver operating characteristic curve was 0.889 and 0.682, respectively (Figure 1). Sensitivity/specificity without/with 5-fold cross validation was 0.888/0.789 and 0.794/0.535, respectively. Conclusion: A parsimonious set of 10 variables associated with EPI in CP was identified. The CART decision tree demonstrated good prediction performance for EPI and was selected as the most practical screening tool to aid clinicians in the assessment of EPI in patients with CP. Further external validation of the EPI screening tool is planned.Figure 1.: AUC-ROC of the CART model trained on the full analysis set and after 5-fold cross-validation. AUC, area under the curve; CART, classification and regression tree; CV, cross-validation; ROC, receiver operating characteristic.

India is ranked 120th among 122 countries globally in WaterAid’s water quality index. Regular water quality monitoring is essential to determine which inland water bodies are experiencing depreciating water quality. Long-term trends have been obtained using satellite remote sensing, necessitating multiple image analysis. The computational burden of processing numerous satellite images can be reduced using Google Earth Engine’s (GEE) cloud computing capabilities. Thorough research was conducted to determine the global spatio-temporal and biochemical factors impacting surface water quality (WQ). The public availability of geospatial datasets and free access to cloud-based geo-computing platforms such as GEE are widely used for spatio-temporal mapping, global surface water monitoring, water quality parametric variation, and real-time forecasting. Many researchers have recently focused on improving data mining and machine learning algorithms to accurately deal with image classification and predictive problems for crop identification and monitoring. In the present study, we propose to define band spectral ratios, spectral band equations, and empirical models for water quality parameters. A few neural network models will be used to (i) query and pre-process satellite earth observations that coincide with the study area, (ii) extract the spectra, and (iii) use spectral band wavelength charts, time-series charts, spatial-distribution maps, and the development of an online dashboard application to visualize the results graphically upon using integrated Landsat (8,9), Sentinel-2A/B and PlanetScope satellite data for the pre-monsoon and post-monsoon seasons in 2023 in the pan-India region. MODIS-TERRA provides LST spatial-temporal monitoring. In this, we have assessed and compared the performance of CART, SVM, and RF ML algorithms. We found that RF outperforms CART and SVM algorithms in the GEE platform with PlanetScope data (80.71% overall accuracy (OA) with Kappa 0.89) and also with the integration of PlanetScope and Sentinel-2A/B data (OA = 85.53%, Kappa 0.91). But CART outperforms RF and SVM algorithms with Sentiel-2A/B data (OA = 81.59%, Kappa 0.85). The SAM technique, spectral feature fitting, continuum band removal, and other band-spectral ratio techniques are employed for quantitative hyperspectral data analysis. Specifically, the performance metrics of XGBoost and SGD for both Chl-a (R^2 = 0.818) and Turbidity (R^2 = 0.815) models exhibited robust accuracy. We have also developed a Google API-based JavaScript code that can be tested under complex coastal shores, challenging inundation and variable climatic conditions. Our method provides the end-to-end cloud computing workflow shown in this research, considering cost and computational efficiency for timely information delivery.Keywords: Classification and Regression Trees (CART), Chlorophyll-a (conc), Color dissolved organic matter (CDOM), Composite water management index (CWMI), Environmental Mapping and Analysis Program (EnMAP), Land Surface Temperature (LST), Machine Learning (ML), Random Forest (RF), Spectral Angle Mapper (SAM), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Total Suspended Solids (TSS), Water Quality (WQ), XGBoost (Extreme Gradient Boosting).

Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

Objective: To investigate the potential of Classification and Regression Trees (CARTs) for the diagnosis of thyroid lesions based on cell block immunocytochemistry and cytological outcome. Study Design: A total of 956 histologically confirmed cases (673 benign and 283 malignant) from patients with thyroid nodules were prepared via liquid-based cytology and evaluated; 4 additional slides were stained for cytokeratin 19 (CK-19), galectin 3 (Gal-3), Hector Battifora mesothelial cell 1 (HBME-1), and thyroglobulin. On the basis of immunocytochemistry and the cytological diagnosis, a CART algorithm was constructed and used for evaluation. Results: The major important factors contributing to the diagnostic CART model were: cytological outcome, CK-19, Gal-3, and HBME-1. The sensitivity and specificity of the cytological diagnosis were 96.27% and 88.26%, respectively (cut-off: category 3 of The Bethesda System [TBS-3]). The introduction of immunocytochemistry and the CART model increased the sensitivity and specificity to 98.88% and 99.11%, respectively. CK-19 presented the best performance for discriminating papillary thyroid carcinomas, followed by HBME-1 and Gal-3. In the TBS-2 cases, CK-19 and, subsequently, Gal-3 were important immunocytochemistry markers. Ultimately, CK-19 and HBME-1 on TBS-5 or TBS-6 cases demonstrated the best results. Conclusions: The hierarchical structure of the CART model provides a diagnostic algorithm linked with the risk of malignancy at every step of the procedure. It also provides guidance on the use of ancillary examinations as it goes by simple, human understandable rules.

Land subsidence modelling using tree-based machine learning algorithms

Weed infestations have been identified as a major cause of yield reductions in canola (Brassica napus L.), a vital oil crop that has gained significant prominence in Iran, especially within Fars Province. Weed management using machine learning algorithms has become a crucial approach within the framework of precision agriculture for enhancing the efficacy and efficiency of weed control strategies. The evolution of habitat suitability models for weeds represents a significant advancement in agricultural technology, offering the capability to predict weed occurrence and proliferation accurately and reliably. This study focuses on the issue of dominant weed infestation in canola cultivation, particularly emphasizing the prevalence and impact of wild oat (Avena fatua L.) as the dominant weed species in canola farming in 2023. We collected data on 12 environmental variables related to topography, climate, and soil properties to develop habitat suitability models. Three machine learning techniques, including random forest (RF), support vector machine (SVM), and boosted regression tree (BRT), were estimated based on the receiver operating characteristic (ROC) and area under the curve (AUC) to model the distribution of A. fatua. Model performance was quantified using the ROC curve and AUC metrics to identify the best predictive algorithm. The findings indicated that RF, BRT, and SVM models exhibited accuracies of 99%, 97%, and 96% for the habitat suitability of A. fatua, respectively. The Boruta feature selection method identified the slope variable as significantly influential in A. fatua habitat suitability modeling, followed by plan curvature, clay, temperature, and silt. This study serves as a case study that highlights the utility of machine learning for habitat suitability predictions when information on multiple environmental variables is available. This approach supports effective weed management strategies, potentially enhancing canola productivity and mitigating the ecological impacts associated with weed infestation.

Groundwater potential mapping (GWPM) in the coastal areas is central for the decion making and development of the society and the environment. The current study endeavours the delineation of the groundwater potential zones of Korba coastal aquifer in the Cap-Bon Peninsula in the North East of Tunisia, using three different machine Learning techniques : random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 17 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI),Stream Power Index,Drainage Density and rainfall were considered for inter-thematic correlations and overlaid with wells locations and Transmissivity data in a spatial database. A total of 225 wells locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (92%) and hybrid model (89.2%) was more efficient than BRT (85.6%) model. The results of the study will help the decision-makers, government agencies, and private sectors for sustainable planification of  groundwater resources in the study area.

Emotion is an important element in expressive speech synthesis. Unlike traditional discrete emotion simulations, this paper attempts to synthesize emotional speech by using strong, medium, and weak classifications. This paper tests different models, a linear modification model (LMM), a Gaussian mixture model (GMM), and a classification and regression tree (CART) model. The linear modification model makes direct modification of sentence F0 contours and syllabic durations from acoustic distributions of emotional speech, such as, F0 topline, F0 baseline, durations, and intensities. Further analysis shows that emotional speech is also related to stress and linguistic information. Unlike the linear modification method, the GMM and CART models try to map the subtle prosody distributions between neutral and emotional speech. While the GMM just uses the features, the CART model integrates linguistic features into the mapping. A pitch target model which is optimized to describe Mandarin F0 contours is also introduced. For all conversion methods, a deviation of perceived expressiveness (DPE) measure is created to evaluate the expressiveness of the output speech. The results show that the LMM gives the worst results among the three methods. The GMM method is more suitable for a small training set, while the CART method gives the better emotional speech output if trained with a large context-balanced corpus. The methods discussed in this paper indicate ways to generate emotional speech in speech synthesis. The objective and subjective evaluation processes are also analyzed. These results support the use of a neutral semantic content text in databases for emotional speech synthesis

Machine learning approach to predict susceptible growth regions of Moringa peregrina (Forssk)

Optimized modelling of countrywide soil organic carbon levels via an interpretable decision tree

BackgroundCommunity-based health insurance (CBHI) is a vital tool for achieving universal health coverage (UHC), a key global health priority outlined in the sustainable development goals (SDGs). Sub-Saharan Africa continues to face challenges in achieving UHC and protecting individuals from the financial burden of disease. As a result, CBHI has become popular in low- and middle-income countries, including Ethiopia. Therefore, this study aimed to identify the ML algorithm with the best predictive accuracy for CBHI enrollment and to determine the most influential predictors among the dataset.MethodsThe 2019 Ethiopian Mini Demographic and Health Survey (EMDHS) data were used. The CBHI were predicted using seven machine learning models: linear discriminant analysis (LDA), support vector machine with radial basis function (SVM), k-nearest neighbors (KNN), classification and regression tree (CART), and random forest (RF). Receiver operating characteristic curves and other metrics were used to evaluate each model’s accuracy.ResultsThe RF algorithm was determined to be the best machine learning model based on different performance assessments. The result indicates that age, wealth index, household members, and land usage all significantly affect CBHI in Ethiopia.ConclusionThis study found that RF machine learning models could improve the ability to classify CBHI in Ethiopia with high accuracy. Age, wealth index, household members, and land utilization are some of the most significant variables associated with CBHI that were determined by feature importance. The results of the study can help health professionals and policymakers create focused strategies to improve CBHI enrollment in Ethiopia.

False Financial Statements (FFS) have long been a serious problem in China and other Asian countries, which significantly dampen the confidence of the investors. Regardless of listed companies or non-listed companies, the percentage of financial statements that contained false information is quite high, which is one of the major reasons why China stock markets moved in the opposite direction towards its wonderful economic growth over the past few years. The objective of this research is to introduce one statistical technique — Classification and Regression Tree (CART), to identify and predict the impacts of FFS. We survey financial statements manipulation tricks, FFS indicators and FFS detection techniques from both China and international perspective, and further look into ten listed companies with known FFS history in China; combining these findings, we propose key indicators to work with CART. Our analysis includes 24 false financial reports, and 124 non-false financial reports. We use CART to develop two FFS detecting models: CART without industry benchmark and CART with industry benchmark. For supporting comparison, we also build a Logit regression which is a commonly used technique in FFS detecting. We find that CART is effective in distinguishing FFS from non-FFS. Both CART models achieve better accuracy in identifying fraud cases and making predictions than Logit regression does, and CART with industry benchmark is slightly better than CART without benchmark, but it does not always have superior performance. Our CART model also tries to capture the indicators and their combinations that could reflect firms with high possibility of FFS in China.

To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed. Retrospective cohort study.Setting. Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil. Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, "test sample") and the other used for validation (ie, "validation sample"). The area under the receiver-operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid. Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

This study aims to provide useful information for the conservation and management of Brachydiplax chalybea flavovittata (Ris, 1911) (Insecta: Odonata), which is a climate-sensitive biological indicator species, and to develop a more accurate and optimal distribution model that aligns with the research objectives. To achieve this, we intend to evaluate two factors that influence the prediction accuracy of the distribution model. In order to compare the prediction accuracy of the models based on biased location data in a specific region and based on the multicollinearity of environmental variables known as bioclimatic variables, the confirmed habitat locations of B. chalybea flavovittata were collected from the GBIF database, and 19 biological climate variables used as environmental variables were collected from the Worldclim database. The bias of the location data was examined using four types of data selected with a certain distance between the location data. The multicollinearity of the environmental variables was assessed using the Variation Inflation Factor (VIF). Preprocessed data were applied to seven species distribution models, and the prediction accuracy of the models was compared and analyzed using three evaluation metrics (Kappa, TSS, AUC). Among machine learning and regression models, seven distribution models were used for comparison. The machine learning models included Boosted Regression Trees (BRT), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and MaxEnt, while the regression models included Generalized Linear Models (GLM) and Multivariate Adaptive Regression Splines (MARS). Overall, the prediction accuracy of the models was better explained by the Random Forest (RF) model, which is a machine learning method that better captures the complex relationship between species and environmental variables, compared to other models. The bias of location data, which is more commonly surveyed in easily accessible areas, affected the prediction accuracy in all models, while multicollinearity varied depending on the model. This study is expected to serve as fundamental guidance for developing an optimal species distribution model that aligns with the research objectives of species conservation and it can be utilized as foundational data for effective management of wildlife habitat in national parks.