Data augmentation for disruption prediction via robust surrogate models

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

Development of machine learning models for prediction of antibody non-specificity.

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

College graduates earn substantially more and are more likely to be employed. Consequently, it is critically important to understand the predictors of college dropout so that students and administrators can make a difference in college graduation outcomes. Previous studies remain limited in the scope of evaluating machine learning models for dropout prediction. Leveraging a dataset of 4,424 students that includes graduation outcome, demographic, socioeconomic and course data, and macroeconomic data, the objective of this paper is to identify the optimum machine learning model for predicting college dropout as a classification problem. We (a) perform extensive exploratory data analysis, (b) perform feature optimization (c) identify the best performing machine learning model across seven models evaluated, (d) study different testing-to-training ratios, (e) perform a comprehensive model evaluation, and (f) compare a multi-class classification approach to a binary classification one. The models were fine-tuned leveraging a grid search optimization algorithm and validated with k-fold cross-validation. Optimizing the hyperparameters, the grid search optimized random forest model performed the best in predicting college dropout with 0.85 accuracy, 0.72 sensitivity, 0.92 specificity, 0.82 precision, and 0.89 AUC-ROC. Furthermore, the optimized random forest model suggested the key predictors of dropout, in order of importance to be: number of curricular units in the second semester, number of curricular units in the first semester and whether the tuition and fees are up-to-date. The findings underscore the value of using machine learning for timely dropout risk prediction, enabling targeted resource allocation to mitigate risk and support successful graduation outcomes.

The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding breast cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. The analysis of our experiments shows an advantage of the linear Support Vector Regression, Lasso regression, Kernel Ridge regression, K-neighborhood regression, and Decision Tree regression—these models achieve most accurate survival prognosis results. The cross-validation for accuracy demonstrates best performance of the same models on the studied breast cancer data. As a support for the proposed approach, a Python-based workflow has been developed and the plans for its further improvement are finally discussed in the paper.

Heart failure (HF) is one of the major causes of morbidity and mortality globally, necessitating accurate tools for health outcome prediction and risk stratification. In this study, we propose an interpretable multimodal machine learning framework integrating four clinical data modalities (i.e., demographics, medications, laboratory tests, and electrocardiograms [ECGs]) to predict 30-day all-cause mortality and hospital readmission in HF patients. Using clinical data from 2868 HF patients across 43 local hospitals in Hong Kong, we trained and evaluated ten machine learning models for HF risk prediction, with the best performing model achieving an area under the receiver operating characteristic curve (AUC) of 0.881 for mortality and 0.709 for readmission. Notably, laboratory tests and ECG features dominate predictive power, and their combination alone yielded near-optimal results (AUC: 0.872), suggesting that these two modalities may be adequate for effective risk prediction in resource-constrained settings. The SHapley Additive exPlanations (SHAP) analysis identified serum albumin, high-sensitivity troponin I, lactate dehydrogenase, and QT interval dispersion as key predictors. Feature redundancy analysis further revealed strong correlations within laboratory tests and ECG features, suggesting opportunities for model simplification. To the best of our knowledge, this is the first study that comprehensively evaluates diverse configurations of four data modalities for HF risk prediction through ablation analysis, quantifying the marginal gains of each data modality and their combinations. Our findings demonstrate that an interpretable multimodal machine learning model can enhance risk prediction in HF patients, supporting personalized management and scalable deployment across diverse healthcare settings.

PM2.5 pollution is increasing, causing health problems. The objective of this study was to model the behavior of PM2.5AQI (air quality index) using machine learning (ML) predictive models of linear regression, lasso, ridge, and elastic net. A total of 16,543 records from the Huachac, Junin area in Peru were used with regressors of humidity in % and temperature in °C. The focus of this study is PM2.5AQI and environmental variables. Methods: Exploratory data analysis (EDA) and machine learning predictive models were applied. Results: PM2.5AQI has high values in winter and spring, with averages of 52.6 and 36.9, respectively, and low values in summer, with a maximum value in September (spring) and a minimum in February (summer). The use of regression models produced precise metrics to choose the best model for the prediction of PM2.5AQI. Comparison with other research highlights the robustness of the chosen ML models, underlining the potential of ML in PM2.5AQI. Conclusions: The predictive model found was α = 0.1111111 and a Lambda value λ = 0.150025, represented by PM2.5AQI = 83.0846522 − 10.302222000 (Humidity) − 0.1268124 (Temperature). The model has an adjusted R2 of 0.1483206 and an RMSE of 25.36203, and it allows decision making in the care of the environment.

Recently, great progress has been achieved in diabetes management through minimally invasive continuous glucose monitoring, which involves piercing the skin to identify changes in glucose levels in the interstitial fluid. Nevertheless, the development of accurate fully noninvasive (NI) glucose monitors remains a topic of great interest. This work examines the processing of data collected from a multisensor system, which combines millimeter-wave (mm-wave), near-infrared (NIR), and other auxiliary sensors’ technology, coupled with a random forest machine learning (ML) model for prediction. We investigated possible correlations between mm-wave, NIR, skin temperature, pressure data, and blood glucose values and deployed an ML model for prediction. The model is based on the random forest algorithm that was trained and tested on data collected from a study involving five healthy human subjects undergoing an intravenous glucose tolerance test. Our model achieves a root mean square error of 21.06 mg/dl and a mean absolute relative difference of 7.31% for glucose prediction in the collected clinical subjects dataset. In addition, 96.1% of the model’s predictions fall within the clinically acceptable zones according to the Clarke error grid analysis. The preliminary results suggest that combining multiple sensor data with ML techniques is promising for the development of NI glucose monitoring.

Uveal melanoma (UM) is the most frequent primary malignant intraocular tumor in adults with an estimated incidence of 4-5 per million per year in western countries [1]. About 50% of UM patients eventually develop metastasis. In a previous study, the prognosis of Chinese UM was mainly correlated with visual clinical features and gene sequencing results. Models designed to predict UM prognosis have been previously described [2], but these studies were based on Caucasians, not Chinese. Further, our previous study [3] determined that the clinical characteristics were different between Chinese and Caucasian UM patients. Therefore, in this study, we analyzed the clinical characteristics and survival status of 1553 patients diagnosed with UM over a period of 15 years in China, and also included factors that were not included in our previous study, such as the largest basal diameter, thickness, tumor size after updating the criteria, pigmentation, whether complicated with intraocular hemorrhage, ciliary body involvement, extraocular extension and TNM stage. We also constructed comprehensive prognostic models to predict the risk of metastasis within 2 years and death of UM after 2 years following treatment according to clinical characteristics using machine learning. The methods of this study are described in the Supplementary File. The clinical data of the UM patients are shown in Supplementary Table S1. The mean age of subjects was 47.2 ± 12.5 years (median, 48.0 years; range, 5-85 years), indicating that the onset age is much younger than Caucasians [4] for whom it usually occurs in the fifth to sixth decades. This demonstrates one of the racial variations of the disease. Nearly 70% of patients' visual acuity was worse than the 0.30 logarithm of the minimum angle of resolution (LogMAR). Intraocular pressure was within the normal range of 10-21 mmHg in 1279 (85.6%) patients, while it was lower than 10 mmHg in 148 (9.9%) patients and higher than 21 mmHg in 67 (4.5%) patients. The mean largest basal diameter was 12.0 ± 3.6 mm (median, 11.7 mm; range, 1.1-23.0 mm), and the mean tumor thickness was 7.1 ± 3.3 mm (median, 6.9 mm; range, 0.4-23.0 mm). According to the criteria of the American Joint Committee on Cancer classification (7th edition), there were 198 (12.9%) patients at Stage I, 1089 (71.1%) at Stage II, and 243 (15.8%) at Stage III. It was reported that the mixed cell-type tumor is the most common UM [5], but in the present study, the spindle cell-type UM was the most common pathological type (44.6%). The mean follow-up time was 49.5 ± 32.5 months (median, 43 months; range, 0-197 months). We followed 78.4% of patients for more than 2 years, 32.5% for more than 5 years and 3.2% for more than 10 years. The estimated 5-, 10-, and 15-year overall survival rates after treatment initiation were 84.0%, 72.0%, and 66.7% (Figure 1A). Shields et al [4] published a cohort study of 8100 UM patients, demonstrating UM-related survival rates of 91.4%, 86.5% and 83.8% and metastasis rates of 15%, 25% and 31%. Our 5-, 10- and 15-year UM-related survival rates were 85.6%, 75.5% and 69.5%, and were thus lower than Shields et al results [4]. However, the metastasis rates in our study were higher at 18.9%, 27.2% and 31.4% (Figure 1B). Similarly, our UM-related survival rates were lower than those reported in Scotland (92.3%, 87.4% and 83.8%) [6]. A total of 183 patients in our study died of UM metastasis, and the median duration from metastasis to death was 7 months. 75.5% of these patients died within 1 year of metastasis. A previous study showed that 80% of UM patients died within 1 year of metastasis and 92% within 2 years [7]. The most common site of metastasis observed was the liver (Supplementary Table S2). Multiple-site metastasis occurred in 70 patients. Survival analysis and predicting models of 1553 UM patients. A. Kaplan-Meier curve of overall survival. B. Kaplan-Meier curve of metastasis. C. ROC curve of model for predicting death after 2 years of treatment (UMDeath model). D. ROC curve of model for predicting metastasis within 2 years of treatment (UMMetastasis model). Abbreviations: UM, Uveal Melanoma; AUC, Area Under Curve; ROC, Receiver Operating Characteristic We assessed the variables with significant differences in univariate analysis for Cox multivariate regression analysis (Supplementary Table S3). There were few patients with known pathological types (n = 386), including spindle cell-type (n = 172), mixed cell-type (n = 131), and epithelioid cell-type (n = 83). Thus, pathological information was not included in the multivariate analysis. Using the log-rank test, we found that the prognosis of patients with epithelioid cell-type UM was significantly worse than spindle cell-type UM (Bonferroni corrected P < 0.001); which was consistent with another study [8]. Cox multivariate regression analysis of some other possible prognostic factors showed that older age, larger basal diameter and presence of subretinal fluid were associated with high mortality and metastasis, while hemisphere tumor was associated with better prognosis (Supplementary Figures S1 and S2). In addition, tumor pigmentation and position were only related to death (Supplementary Figure S1). We used random forest [9, 10] to construct two models (UMDeath and UMMetastasis): whether a patient will survive for more than 2 years after treatment and whether the tumor will metastasize within 2 years of treatment using demographic attributes, general ocular features and tumor-specific features, which are listed in Supplementary Table S3. The imbalanced ratio (the ratio between the numbers of majority samples and minority samples) was different when we chose a different censored time, and the ratio was abnormal when the censored time was 3 years. Thus, we chose to predict the prognosis of patients within 2 years. Moreover, four-fold cross-validation was used to fairly assess the performance of random forest, where the training dataset and the testing dataset are subject-independent (each sample was collected from one patient and all samples were split into training and testing datasets in cross-validation, therefore, one sample were used only once in the training or testing dataset). Also, 0.5 was selected as the threshold for both classification problems, on which all statistical results were based. Finally, we applied genetic feature selection to investigate which features were more related to the two classification problems (whether a patient would die after 2 years or have metastasis within 2 years after treatment) (Supplementary Material). When the model for the prediction of death was established, we found that the largest basal diameter, thickness, size, intraocular pressure and initial treatment were related to death. The boxplots show the results of four-fold cross-validation before and after feature selection (Supplementary Figure S3A and S3B). The sensitivity, specificity, accuracy, area under the curve (AUC) and their 95% confidence intervals are shown in Supplementary Table S4. PRiMeUM [2], a model for predicting the risk of metastatic UM, used the clinical characteristics and chromosomal information to determine the risk of metastasis within 2 years after the primary treatment; and showed that the accuracy of risk prediction could reach 83% using only clinical characteristics. Moreover, we established a model for predicting metastasis within 2 years after treatment. It was found that the largest basal diameter, size, stage, age, intraocular pressure and gender were associated with metastasis. The boxplots show four-fold cross-validation results before and after feature selection (Supplementary Figure S4A and S4B). The sensitivity, specificity, accuracy, AUC and their 95% confidence intervals are shown in Supplementary Table S5. Figures 1C and 1D, Supplementary Figures S3C and S4C depict the receiver operating characteristic and precision-recall curves of one-fold out of four-fold cross-validation for predicting death and metastasis, respectively. Two predictive models each found that intraocular pressure was an influencing factor for UM metastasis and death, and intraocular pressure and prognosis were related to tumor volume. The larger the tumor, the greater the impact on the eyeball structure and physiological conditions, and thus is more likely to cause changes in intraocular pressure. This present study is currently the largest retrospective study on UM in China. The age of onset for Chinese UM patients was earlier and the prognosis was worse than Western patients. The largest basal diameter and age were found to be related to prognosis in both statistical results and predictive models. Our predictive models found that intraocular pressure was related to both death and metastasis. These models achieved a high sensitivity and specificity for predicting survival and can predict metastasis to a certain extent. The authors thank those who participated in the present study. Supported by The Capital Health Research and Development of Special (2020-1-2052), Science & Technology Project of Beijing Municipal Science & Technology Commission (Z201100005520045, Z181100001818003), The Beijing Municipal Administration of Hospitals' Ascent Plan (DFL20150201), The National Natural Science Foundation of China (82101180), Beijing Natural Science Foundation (7204245), Scientific Research Common Program of Beijing Municipal Commission of Education (KM202010025018), Beijing Municipal Administration of Hospitals' Youth Programme (QMS20190203), Beijing Dongcheng District Outstanding Talents Cultivating Plan(2018). No conflicting relationship exists for any author. This research adhered to the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board of Beijing Tongren Hospital. We obtained informed consent from all of these patients. YML, YL, and WBW contributed to the study conception. YNC, YNW and MXC designed the current study. HHZ, YNH, YF,YJL and JTL collected the data and implemented the follow-up. KZ, YNC and YNW performed the analyses and verified the underlying data. RTC, RF and HW arranged and checked the consistency of results. YNC and YNW wrote the initial draft of the manuscript. WBW and KZ commented on the manuscript and gave final approval of the version to be published. Written informed consent was obtained from the patients for participation in the study and publication of this article. A copy of the written consent is available for review by the Editor-in-Chief of this journal. Correspondence and requests for materials and code should be addressed to WBW ([email protected]), YL ([email protected]) or YML ([email protected]). Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

BackgroundCardiovascular disease (CVD) is a leading cause of mortality among older adults, yet existing risk prediction tools lack validation for individuals aged ≥ 80 years. This study aims to develop and validate gender-specific machine learning models for CVD risk prediction in adults aged ≥ 80 years using routine biomarkers, and to identify key predictors through interpretable artificial intelligence approaches.MethodsThis prognostic study analyzed two independent longitudinal datasets (2012–2014 and 2014–2018) from the Chinese Longitudinal Healthy Longevity Survey including 1,954 community-dwelling adults aged ≥ 80 years (715 males, 1,239 females) without CVD at baseline. The outcome was incident CVD within 4 years. Five machine learning algorithms (i.e., logistic regression decision tree, support vector machine, random forest, and extreme gradient boosting (XGBoost)) were compared using area under the receiver operating characteristic curve (AUC), recall, specificity, precision, and F1-score. Model interpretability was assessed using SHapley Additive exPlanations values.ResultsThe XGBoost model demonstrated superior performance for both males (AUC [95% confidence interval], 0.751 [0.635–0.855]; recall, 759 [0.593–0.909]; F1-score, 0.506 [0.369–0.628]) and females (AUC, 0.748 [0.671–0.819]; recall, 0.891 [0.791–0.976]; F1-score, 0.463 [0.369–0.551]). Shared top predictors included vitamin D3, glycated albumin, platelet count, and vitamin B12. Risk stratification based on model predictions effectively identified high-risk individuals, with hazard ratios of 7.46 (95% CI, 2.81–19.78) for males and 5.18 (95% CI, 2.27–11.82) for females in the highest risk group compared to the lowest risk group.ConclusionsThis study developed interpretable, gender-specific machine learning models for CVD risk prediction in the oldest old population using routine biomarkers. The models demonstrated good discrimination and calibration, offering a practical tool for identifying high-risk individuals who may benefit from targeted preventive interventions. These findings suggest the potential utility of biomarker-based machine learning approaches in cardiovascular risk assessment for the rapidly growing elderly population.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12877-025-06676-0.

Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.

MACHINE LEARNING PREDICTION OF IN-HOSPITAL DISEASE PROGRESSION IN COMMUNITY-ACQUIRED PNEUMONIA: DERIVATION AND VALIDATION OF CLINICAL DETERIORATION IN PNEUMONIA (CDIP)

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

Rainfall plays a crucial role in agricultural practices, influencing the growth of essential crops and impacting economies reliant on agriculture. In Rajasthan, India, where agriculture is predominantly rainfed and dependent on unpredictable monsoon rains, accurate rainfall forecasting is vital for effective crop management, irrigation, optimizing agricultural productivity, managing water resources, and addressing drought conditions and. This study evaluates various machine learning models for predicting rainfall in East and West Rajasthan. The models assessed include Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and K-Nearest Neighbours (KNN). These models were compared based on their accuracy measures, including Root Mean Squared Error (RMSE), Relative Root Squared Error (RRSE), and Relative Absolute Error (RAE). The results indicate that ANN and KNN perform poorly compared to SVR and RF suggesting their inadequacy for rainfall forecasting in water-scarce regions. The performance of SVR and RF are comparable as the difference is less. However SVR is identified as the most effective model for rainfall prediction in both subdivisions, as highlighted in the study. This study suggest that SVR and RF model should be includes in prediction of amount of rainfall.