Random Forest Machine Learning Model Research Articles

Managed honey bees, Apis mellifera (Hymenoptera: Apidae), face greater risk from parasites and pathogens than mosquito control insecticide applications.

As the primary pollinator for many crops, honey bees (Apis mellifera) are critically important to food production and the agricultural economy. Adult mosquito control is often suspected by the public and commercial beekeepers to harm honey bees, creating conflicts between industries. To investigate this matter, a two-year field study was conducted on vegetated wetlands in Salt Lake City, Utah, U.S.A. where honey bee colonies were placed in areas subjected to aerial adult mosquito control applications using the organophosphate naled. Comparison colonies were placed in areas not exposed to insecticides. Colony conditions were documented over the two-year period to capture both immediate and cumulative season-long effects of naled to honey bee health. A Before-After-Control-Impact (BACI) analysis of mortality data from treated and non-treated colonies using mixed effects models revealed no statistically significant differences, indicating that aerial applications of naled for mosquito control did not adversely affect these honey bee colonies. A Random Forest machine-learning model identified that Nosema infection, maximum temperatures, and seasonal progression were more significant contributors to bee mortality during the study period, whereas cumulative naled applications were among the least significant predictors. Non-parametric statistical tests (NMDS and PERMANOVA) indicated no differences in colony resources (pollen/honey/nectar; open/capped brood) and parasite (Varroa mites; Vairimorpha microsporidians) loads between exposed colonies and non-treatment colonies. These findings were consistent across different seasons and varying environmental conditions. Our results suggest that naled, when used as intended for mosquito control, does not pose a significant risk to managed honey bee populations in rural settings.

Strong wind is one of the important factors that trigger landslides

Strong wind is one of the important factors that trigger landslides

Role of Sentiment in Stock Forecasting

Abstract: The Stocks uses the sentiment analysis in predicting stock market movements by analyzing data from sources like news, social media, and financial reports. Employing a Random Forest machine learning model, the system aggregates sentiment indicators, such as positive and negative emotions, to forecast stock trends and market fluctuations. The results show that sentiment analysis improves prediction accuracy, reduces investment risks, and supports data-driven decision-making. By integrating real-time data, the model adapts to changing market conditions and provides timely forecasts. This approach demonstrates the potential of sentiment-driven analysis to enhance investment strategies and create a proactive tool for financial market analysis.

Sub-hourly forecasting of fire potential using machine learning on time series of surface weather variables

Background Rapidly developing pre-fire weather conditions contributing to sudden fire outbreaks can have devastating consequences. Accurate short-term forecasting is important for timely evacuations and effective fire suppression measures. Aims This study aims to introduce a novel machine learning-based approach for forecasting fire potential and to test its performance in the Sunshine Coast region of Queensland, Australia, over a period of 15 years from 2002 to 2017. Methods By analysing real-time data from local weather stations at a sub-hourly temporal resolution, we aimed to identify distinct weather patterns occurring hours to days before fires. We trained random forest machine learning models to classify pre-fire conditions. Key results The models achieved high out-of-sample accuracy, with a 47% higher accuracy than the standard fire danger index for the region. When simulating real forecasting conditions, the model anticipated 75% of the fires (11 out of 15). Conclusions This method provides objective, quantifiable information, enhancing the precision and effectiveness of fire warning systems. Implications The proposed forecasting approach supports decision-makers in implementing timely evacuations and effective fire suppression measures, ultimately reducing the impact of fires.

Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors

Abstract. Temporally continuous snow depth estimates are vital for understanding changing snow patterns and impacts on permafrost in the Arctic. We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. The model performed well on Alaska's Seward Peninsula where it was trained and at Arctic evaluation sites (RMSE ≤ 0.15 m). It performed poorly at temperate sites with deeper snowpacks, partially due to training data limitations. Small temperature sensors are cheap and easy to deploy, so this technique enables spatially distributed and temporally continuous snowpack monitoring at high latitudes to an extent previously infeasible.

Impact of Urban Expansion on School Quality in Compulsory Education: A Spatio-Temporal Study of Dalian, China

With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure that land development, housing affordability, and school facility investments advance equitable access to quality education. These results provide a novel perspective on compulsory education quality assessment and offer a valuable foundation for guiding education policies and urban development strategies.

Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023: a machine learning algorithm

BackgroundBirth-related mortality is significantly increased by home births without skilled medical assistance during delivery, presenting a major risk to the public’s health.The objective of this study is to predict home delivery and identify the determinants using machine learning algorithm in sub-Saharan African.MethodsThis study used design science approaches. The data set obtained from demographic health survey in sub-Saharan African weighted sample of 299,759 women was included in the stud. Machine learning models such as Random Forest, Decision Tree, K-Nearest Neighbor, Logistic Regression, Extreme Gradient Boosting, AdaBoost, Artificial Neural Network, and Naive Bayes were used. The predictive model was evaluated by area under the curve, accuracy, precision, recall, and F-measure.ResultsThe final experimentation results indicated that random forest model performed the best to predict home delivery with accuracy (83%) and, ROC curve (89%). The Shapley additive explanation features an importance plot optimized for random forest model to identifying the most predictors of home delivery. Association rules findings showed that inadequate antenatal care visits, marital status married, no education, mobile phone, television, electricity, poor wealth index, infrequent television viewing, and rural residence were predictor of home delivery.ConclusionThe random forest machine learning model provides greater predictive power for estimating home delivery risk factors. To reduce the prevalence of home delivery, this finding recommends to emphasis on improving antenatal care services, education, and awareness about health facility delivery.

P0557 Multiomics analysis identifies key pathways associated with severe disease course in children with IBD: a prospective study

Abstract Background Identifying biological pathways that are associated with poor disease course in IBD may improve early risk stratification and guide targeted therapeutic approaches. We therefore aimed to integrate multiomics data determined at disease onset to explore pathways associated with disease course in pediatric IBD, focusing on both microbial and host-derived signals. Methods Children with IBD were enrolled at disease onset and followed prospectively for18 months. Samples for whole exome sequencing (WES), serum metabolomics, stool metabolomics, and microbiome were analyzed using MaAsLin2 for differential expression and Random Forest for outcome prediction for single-omic. A late integration approach with Support Vector Machine was applied for multi-omics prediction. Severe outcome was defined as steroid-dependency, early or multiple biologics, or IBD-related hospitalizations/surgeries. Pathway enrichment analyses for each omic were assessed using Fisher’s exact test and odd ratio (OR) was calculated to asses the enrichment magnitude. Results Altogether, 182 children were enrolled (123 with CD (28% severe course) and 59 UC (39% severe course). Several pathways were differentially enriched in the severe group (Figure 1). Shared pathways across CD and UC included AGE-RAGE signaling, implicated in oxidative stress and inflammation (OR=36 [95%CI 3-459] in CD and OR=92 [7-1121] in UC), and choline metabolism in cancer, associated with lipid signaling, inflammation, and fibrosis (OR=37 [95%CI 4-382] and OR=66 [3-475]). CD-specific pathways included sphingolipid metabolism (OR=30 [95%CI 2-393]), linked to barrier dysfunction and chronic inflammation, glycerolipid metabolism (OR=11 [95%CI 1-160]), and fat digestion and absorption (OR=16 [95%CI 3-167]), highlighting disruptions in nutrient absorption and inflammation. UC-specific pathways included pathways in cancer (OR=23 [95%CI 3-263]), reflecting tissue remodeling and neoplasia risk, inflammatory regulation of TRP channels (OR=14 [95%CI 1-143]), contributing to colonic inflammation, sensory perception and purine metabolism (OR=5 [95%CI 0.4-58]). Random Forest machine learning models showed modest to no predictive performance of the different omics (area under ROC curve ranged between 0.41-0.67 in CD, and 0.38-0.72 in UC; Table 1). Conclusion Key biological pathways were identified in association with severity of disease course in pediatric IBD, utilizing large datasets of genetics, metabolomics and microbiome. The findings offer insights into mechanisms underlying pediatric IBD and highlight potential therapeutic targets as well as biomarkers for early intervention.

P0250 Circulating blood matrix metalloproteinase (MMP)-10 and 12 can accurately predict mucosal healing in Inflammatory Bowel Disease (IBD) in a multi-centred prospective longitudinal cohort study (2020-2024)

Abstract Background Complete mucosal healing (CMH) is a key prognostic indicator of long-term remission in IBD. There are however, no early biomarkers of mucosal healing to allow for deeper biological studies and potential for treatment stratification. Here, we prospectively investigate the clinical utility of a novel custom-designed 21-proteomic PEA-Olink* Flex panel in blood to identify proteins with the propensity for mucosal healing. Methods In the multi-centre, prospective, longitudinal IBD study (www.musicstudy.uk), we used a custom selected 21-protein marker panel (based on roles in resolution of inflammation; Olink Flex [Figure 1]) in blood plasma (1µl), collected at timepoints T1, T2 and T3 (months 0, +3 and +6 respectively). This uses a proximity extension assay, allowing for highly specific and sensitive absolute protein quantification. All subjects had prospective endoscopic assessment T1→T2→T3 (follow-up mean 5.2 months). CMH was defined by a Mayo score of 0 in UC and SES-CD of 0 in CD. Statistical analysis was performed using R, adjusting for age and gender. A machine learning (ML) model using SHAP (SHapley Additive exPlanations) analysis was then applied to the data. Results A total of 239 blood samples from 81 IBD patients were analysed (median 37 years, 59% male; 37% UC) – all with paired endoscopic mucosal healing data. 27/81 patients (33%) achieved CMH at follow-up. Of 21 blood proteins, multivariate analysis showed MMP10 and MMP12 concentrations to be significantly associated with CMH at timepoint 3 (FDR p=0.02 for both) with AUC 0.68 and 0.64 respectively; comparable to faecal calprotectin (AUC 0.69) in predicting mucosal healing. Both proteins were significantly lower in the UC CMH group vs no CMH (p=0.01 respectively), with only MMP10 (p=0.02) being associated with CMH in CD alone. In UC, endoscopic severity score UCEIS at follow-up endoscopy, correlated strongly with MMP10 and MMP12 levels combined (p<0.001, r=0.62). In UC, ΔMMP10 levels over time were highly significant in predicting improvement in endoscopic scores; but not CD. Random Forest ML model (incorporating 80 clinical variables including drug treatment) to the 21 proteins from T1→T2→T3 predicted CMH with an AUC of 0.69. SHAP analysis identified MMP12 in the top 3 variables impacting the model’s prediction. Conclusion Our study identifies tissue remodelling proteins MMP-10 and 12 as biomarkers for CMH on par with faecal calprotectin. The ΔMMP10 expression in UC shows dynamic change, predicting mucosal healing with more significance over time. MMP-10 and 12 are involved in the tissue degradation and remodelling process of inflamed mucosa which may explain the reduction in blood levels as mucosal inflammation resolves. Further large-scale validation work is planned. References *PEA-Olink: Proximity Extension Assay

Predicting and mapping soil saturated hydraulic conductivity in the Beni Moussa irrigated perimeter (Tadla Plain, Morocco) using Random Forest machine learning model

Predicting and mapping soil saturated hydraulic conductivity in the Beni Moussa irrigated perimeter (Tadla Plain, Morocco) using Random Forest machine learning model

Where We Rate: The Impact of Urban Characteristics on Digital Reviews and Ratings

In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making.

Lateral End-Range Movement Profile and Shot Effectiveness During Grand Slam Tennis Match-Play.

End-range movements are among the most demanding but least understood in the sport of tennis. Using male Hawk-Eye data from match-play during the 2021-2023 Australian Open tournaments, we evaluated the speed, deceleration, acceleration, and shot quality characteristics of these types of movement in men's Grand Slam tennis. Lateral end-range movements that incorporated a change of direction (CoD) were identified for analysis using k-means (end-range) and random forest (CoD) machine learning models. Peak speed, average deceleration into the CoD, average reacceleration out of the CoD, and the quality of the shot played were computed. Players were grouped based on their ATP rankings (top 10, top 50, and outside top 50) to examine the influence of ranking on movement profiles and shot effectiveness. Our data showed that end-range movements profiles of top 10 and top 50 players were characterized by higher peak speed (d=0.3-0.88), deceleration intensity (d=0.25-0.63), and acceleration intensity (d=0.06-0.51) when compared to players outside the top 50 (p<0.05). Top 10 players also demonstrated greater peak speeds (d=0.59) and acceleration intensities (d=0.45) compared to top 50 players (p<0.05). There was a nonlinear inverse relationship between peak speed and shot quality, such that, as peak speed increased, shot quality decreased-notwithstanding that top 10 players were more likely to hit high-quality shots at higher peak speeds. These results quantify the discrete kinematic characteristics of the sport's most challenging movement sequence and reveal, for the first time, that higher ranked players may possess superior movement potential on court.

Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area

IntroductionSurface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.MethodsSBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models—Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.ResultsDeformation rates for riverside urban ground, road slopes, and ancient landslides were −3.48 ± 2.91 mm/yr, −5.19 ± 3.62 mm/yr, and −6.02 ± 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.DiscussionReservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.ConclusionsThis study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.

High‐throughput phenotyping of stay‐green in a sorghum breeding program using unmanned aerial vehicles and machine learning

AbstractAs climate change continues to influence global weather patterns, the frequency and severity of drought conditions are expected to increase, posing a significant challenge to crop production. In sorghum (Sorghum bicolor L. Moench), a key cereal crop, the stay‐green trait is of particular importance as a measure of how well a genotype can tolerate post‐anthesis drought conditions, which are critical for harvestable yield. Despite its importance, there is a pressing need for a more efficient, accurate, and precise method to phenotype stay‐green in sorghum to enhance breeding efforts. To address this need, this study explores the application of random forest and XGBoost machine learning models for phenotyping the stay‐green trait in sorghum. These models provide quantitative measurements that have the potential to enhance genomic studies and offer additional benefits. Although correlations with vegetation indices were occasionally high, they were not sufficiently reliable to be used exclusively. The machine learning models, in contrast, showed high percentages of genetic variation explained and had high repeatability. The values generated by these algorithms enable plant breeders to efficiently make selections in their stay‐green breeding programs. Further research is needed to assess the robustness of these models across different environments and genetic material. Additionally, comparing these models with other machine learning approaches will help determine if decision tree‐based models are the most effective for this application. Overall, the models presented in this study serve as a promising foundation for improving the efficiency of stay‐green breeding programs in sorghum, but they require further validation and comparison with alternative approaches.

Understanding and Modeling the Dynamics of Storm‐Time Atmospheric Neutral Density Using Random Forests

AbstractAtmospheric neutral density is a crucial component to accurately predict and track the motion of satellites. During periods of elevated solar and geomagnetic activity atmospheric neutral density becomes highly variable and dynamic. This variability and enhanced dynamics make it difficult to accurately model neutral density leading to increased errors which propagate from neutral density models through to orbit propagation models. In this paper we investigate the dynamics of neutral density during geomagnetic storms. We use a combination of solar and geomagnetic variables to develop three Random Forest machine learning models of neutral density. These models are based on (a) slow solar indices, (b) high cadence solar irradiance, and (c) combined high‐cadence solar irradiance and geomagnetic indices. Each model is validated using an out‐of‐sample data set using analysis of residuals and typical metrics. During quiet‐times, all three models perform well; however, during geomagnetic storms, the combined high cadence solar iradiance/geomagnetic model performs significantly better than the models based solely on solar activity. The combined model capturing an additional 10% in the variability of density and having an error up to six times smaller during geomagnetic storms then the solar models. Overall, this work demonstrates the importance of including geomagnetic activity in the modeling of atmospheric density and serves as a proof of concept for using machine learning algorithms to model, and in the future forecast atmospheric density for operational use.

Solar Radiation Prediction Using Decision Tree and Random Forest Models in Open-Source Software

The present research focuses on solar radiation prediction, which is important for energy production in thermal and solar systems. For this purpose, open-source software (Python) and a methodology involving the creation, implementation, and testing of specific machine learning models random forest (RF) and decision tree (DT) were used. The metrics used to identify the effectiveness of the models in predicting solar radiation were the coefficient (R2), the mean square error (MSE), and the mean absolute error (MAE). The evaluation of the two methods is presented in three cases: for one, two, and seven days. The results show that the RF model has better results than the DT, with MAE and MSE values of 36.96 and 4238.77, respectively, and a determination coefficient of 0.96. The study emphasizes the importance of selecting the appropriate model based on the prediction horizon to estimate solar availability and improve solar and thermal energy system planning.

An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.

머신러닝을 활용한 범죄 수법에 따른 마약류 종류 분류

This study adopts a judgment-based approach to classify drug types based on their associated criminal modus operandi. A total of 105 drug transaction cases, extracted from legal judgments, were analyzed. Each case includes key variables such as the method of contact, payment method, transaction location, delivery method, and the specific type of drug involved. Three machine learning models—Random Forest, XGBoost, and Support Vector Machine (SVM)—were employed to classify drug types according to crime methods. Model performance was evaluated using accuracy, precision, recall, and F1-score. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were used to interpret the influence of each crime method on the model's predictions. The results demonstrated that all three machine learning models exhibited strong predictive performance, with Random Forest and XGBoost achieving particularly high accuracy. These models identified the methods of contact and delivery as the most influential predictors. However, the SVM model, while comparable in predictive accuracy, showed limitations in its ability to interpret key variables effectively.

Advanced machine learning techniques for fake news detection: A comprehensive analysis

The rise of fake news has become a significant global concern, undermining public trust and information integrity. This study explores the application of advanced machine learning algorithms for detecting fake news, leveraging a balanced dataset of real and fake news articles. Through rigorous preprocessing, including text cleaning and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, the study enhances data quality and model performance. Five machine learning models—Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression, and Naïve Bayes—are systematically evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that the Random Forest Classifier outperforms other models with an accuracy of 99.95% and balanced performance across metrics, demonstrating its robustness in distinguishing fake from real news. SVM and Neural Networks also achieve high accuracy, showcasing their capability in handling complex data. Logistic Regression and Naïve Bayes, while computationally efficient, exhibit relatively lower performance. The findings underscore the importance of ensemble methods and sophisticated preprocessing techniques in detecting fake news effectively. This research provides a methodological framework for scalable fake news detection, offering valuable insights for developing automated systems to combat misinformation and promote informed decision-making in the digital age.

An intelligent predictive maintenance system based on random forest for addressing industrial conveyor belt challenges

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regression, neural networks, decision trees, and gradient boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominance of the random forest model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings.