Random Forest Ensemble Research Articles

Evaluating the Performance of Heterogeneous and Homogeneous Ensemble-based Models for Twitter Spam Classification

Spam based attacks are growing in various social networks. Social network spam is a type of unwanted content that appears on social networking sites, such as Facebook, Twitter, Instagram, and others. This study used two categories of ensemble algorithms to build Twitter spam classification models. These algorithms worked by combining the strengths of individual learning algorithms and then reporting their total performances. In ensemble learning, models are formed from data based on the assumption that combining the output of multiple models is better than using a single classifier. Hence, this study used a labeled public dataset for machine learning-based Twitter spam detection. Several studies have investigated the classification of Twitter spam from the available datasets. However, there is a paucity of works that investigated how machine learning-based models, built with homogenous and heterogeneous algorithms, behave in Twitter spam classification. ANOVA-F test was used for selecting the most promising features in the dataset. Then, homogeneous tree-based Random Forest (RF) ensemble and a heterogeneous ensemble vote classifier were employed for the classification of Twitter spam. Tree-based algorithms were used to build a homogeneous twitter spam detection model, while a combination of Support Vector Machine (SVM) and Decision Tree (DT) algorithms was used for building the heterogeneous model (using maximum voting classifier). The current study found that the performance of the Twitter spam detection models were promising. In all, the heterogeneous model recorded better performance with regards to accuracy, precision, recall, and F1-score than the model built with homogeneous base classifier.

Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India

The soil–environmental relationship identified and standardised over the years has expedited the growth of digital soil-mapping techniques; hence, various machine learning algorithms are involved in predicting soil attributes. Therefore, comparing the different machine learning algorithms is essential to provide insights into the performance of the different algorithms in predicting soil information for Indian landscapes. In this study, we compared a suite of six machine learning algorithms to predict quantitative (Cubist, decision tree, k-NN, multiple linear regression, random forest, support vector regression) and qualitative (C5.0, k-NN, multinomial logistic regression, naïve Bayes, random forest, support vector machine) soil information separately at a regional level. The soil information, including the quantitative (pH, OC, and CEC) and qualitative (order, suborder, and great group) attributes, were extracted from the legacy soil maps using stratified random sampling procedures. A total of 4479 soil observations sampled were non-spatially partitioned and intersected with 39 environmental covariate parameters. The predicted maps depicted the complex soil–environmental relationships for the study area at a 30 m spatial resolution. The comparison was facilitated based on the evaluation metrics derived from the test datasets and visual interpretations of the predicted maps. Permutation feature importance analysis was utilised as the model-agnostic interpretation tool to determine the contribution of the covariate parameters to the model’s calibration. The R2 values for the pH, OC, and CEC ranged from 0.19 to 0.38; 0.04 to 0.13; and 0.14 to 0.40, whereas the RMSE values ranged from 0.75 to 0.86; 0.25 to 0.26; and 8.84 to 10.49, respectively. Irrespective of the algorithms, the overall accuracy percentages for the soil order, suborder, and great group class ranged from 31 to 67; 26 to 65; and 27 to 65, respectively. The tree-based ensemble random forest and rule-based tree models’ (Cubist and C5.0) algorithms efficiently predicted the soil properties spatially. However, the efficiency of the other models can be substantially increased by advocating additional parameterisation measures. The range and scale of the quantitative soil attributes, in addition to the sampling frequency and design, greatly influenced the model’s output. The comprehensive comparison of the algorithms can be utilised to support model selection and mapping at a varied scale. The derived digital soil maps will help farmers and policy makers to adopt precision information for making decisions at the farm level leading to productivity enhancements through the optimal use of nutrients and the sustainability of the agricultural ecosystem, ensuring food security.

Prediction of oral food challenge outcomes via ensemble learning

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study.

Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling

The ensemble random forest filter (ERFF) is presented as an alternative to the ensemble Kalman filter (EnKF) for inverse modeling. The EnKF is a data assimilation approach that forecasts and updates parameter estimates sequentially in time as observations are collected. The updating step is based on the experimental covariances computed from an ensemble of realizations, and the updates are given as linear combinations of the differences between observations and forecasted system state values. The ERFF replaces the linear combination in the update step with a non-linear function represented by a random forest. This way, the non-linear relationships between the parameters to be updated and the observations can be captured, and a better update produced. The ERFF is demonstrated for log-conductivity identification from piezometric head observations in several scenarios with varying degrees of heterogeneity (log-conductivity variances going from 1 up to 6.25 (ln m/d)2), number of realizations in the ensemble (50 or 100), and number of piezometric head observations (18 or 36). In all scenarios, the ERFF works well, reconstructing the log-conductivity spatial heterogeneity while matching the observed piezometric heads at selected control points. For benchmarking purposes, the ERFF is compared to the restart EnKF to find that the ERFF is superior to the EnKF for the number of ensemble realizations used (small in typical EnKF applications). Only when the number of realizations grows to 500 the restart EnKF can match the performance of the ERFF, albeit at more than double the computational cost.

Software Fault Detection using Multi-Distinguished-Features Sampling with Ensemble Random Forest Classifier

Software Fault Detection using Multi-Distinguished-Features Sampling with Ensemble Random Forest Classifier

Machine Learning approach for Prediction of residual energy in batteries

The ability to predict battery residual life (RL) in advance is critical for ensuring a reliable supply of energy and the most efficient use of that energy. When it comes to precisely predicting the level of charge of batteries, battery management systems must be durable and trustworthy (SoC). Because of the non-linear nature of battery depreciation, it is extremely difficult to predict SoC estimation with considerably less degradation than is now possible. In this paper, we tend to reduce the data degradation for the prediction of RL using ensemble random forest model. The model enables collection of data, pre-processing and classification using random forest and ensemble random forest for the prediction of RL. The simulation is conducted in terms of R2 and root mean square error (RMSE). The simulation shows that the ensemble random forest model achieves higher prediction accuracy.

Airtightness evaluation of Canadian dwellings and influencing factors based on measured data and predictive models.

The airtightness of buildings has a significant impact on buildings' energy efficiency, maintenance and occupant comfort. The main goal of this study is to provide an evaluation of the air leakage characteristics of dwellings in different regions in Canada. This study evaluated the key influencing factors on airtightness performance based on a large set of measured data (73,450 dwellings located in Canada with 11 measurement parameters for each). Machine learning models based on multivariate regression (MVR) and Random Forest Ensemble (RFE) were developed to predict the air leakage value. The RFE model, which shows better results than MVR, was used to evaluate the effect of the ageing of buildings. Results showed that the maximum increase in air leakage occurs during the first year after construction - approximately 25%, and then 3.7% in the second year, after which the increase rate becomes insignificant and relatively constant - approximately 0.3% per year. The findings from this study can provide significant information for building designs, building performance simulations and strengthening standards and guidelines policies on indoor environmental quality.

Inhibiting Webshell Attacks by Random Forest Ensembles with XGBoost

Malign websites effectively endorse the evolution of web illicit events and force the progression of Web services. As an efficient outcome, there is powerful enthusiasm to create systemic resolutions in inhibiting the client from the call onto such Websites. Knowledge-centered Random Forest outfits with XGBoost tactic is recommended for categorizing Websites into 3 categories: Benign, Spam and Malicious. This practice evaluates the Uniform Resource Locator in the situation deprived of accessing the matter of Websites. Thus, it wipes out the run-time expectation and the likelihood of uncovering clients to the browser aimed susceptibilities. As a consequence of involving Random Forest Ensembles with XGBoost, it realizes superior enactment on expansive view and publicity correlated with blacklisting amenity. Preprocessing is performed in order to improve the quality of the data subsequently, analyze certain algorithms, thereby explore the best model are the facts discussed in this research. Work also continues to probe how well this chosen archetypal will operate in the future ahead.

Identifying Barriers to Successful Completion of Video Telemedicine Visits in Urology

ObjectiveThe utilization of video telemedicine has dramatically increased due to the COVID-19 pandemic. However, significant social and technological barriers have led to disparities in access. We aimed to identify factors associated with patient inability to successfully initiate a video visit across a high-volume urologic practice. Materials and MethodsVideo visit completion rates and patient characteristics were extracted from the electronic medical record and linked with census-level socioeconomic data. Associations between video visit failure were identified using multivariate regression modeling and random forest ensemble classification modeling. ResultsSix thousand eighty six patients and their first video visits were analyzed. On multivariate logistic regression analysis, Hispanic or Latino patients (OR 0.52, 95%CI 0.31-0.89), patients insured by Medicare (OR 0.46, 95%CI 0.26-0.79) or Medicaid (OR 0.50, 95%CI 0.29-0.87), patients of low socioeconomic status (OR 0.98, 95%CI 0.98-0.99), patients with an un-activated MyChart patient portal (OR 0.43, 95%CI 0.29-0.62), and patients unconfirmed at appointment reminder (OR 0.68, 95%CI 0.48-0.96) were significantly associated with video visit failure. Patients with primary diagnosis category of men's health (OR 47.96, 95%CI 10.24-856.35), and lower urinary tract syndromes (OR 2.69, 95%CI 1.66-4.51) were significantly associated with video visit success. Random forest analyses identified insurance status and socioeconomic status as the top predictors of video visit failure. ConclusionAn analysis of a urology video telemedicine cohort reveals clinical and demographic disparities in video visit completion and priorities for future interventions to ensure equity of access. Our study further suggests that specific urologic indications may play a role in success or failure of video visits.

Bus Single-Trip Time Prediction Based on Ensemble Learning.

The prediction of bus single-trip time is essential for passenger travel decision-making and bus scheduling. Since many factors could influence bus operations, the accurate prediction of the bus single-trip time faces a great challenge. Moreover, bus single-trip time has obvious nonlinear and seasonal characteristics. Hence, in order to improve the accuracy of bus single-trip time prediction, five prediction algorithms including LSTM (Long Short-term Memory), LR (Linear Regression), KNN (K-Nearest Neighbor), XGBoost (Extreme Gradient Boosting), and GRU (Gate Recurrent Unit) are used and examined as the base models, and three ensemble models are further constructed by using various ensemble methods including Random Forest (bagging), AdaBoost (boosting), and Linear Regression (stacking). A data-driven bus single-trip time prediction framework is then proposed, which consists of three phases including traffic data analysis, feature extraction, and ensemble model prediction. Finally, the data features and the proposed ensembled models are analyzed using real-world datasets that are collected from the Beijing Transportation Operations Coordination Center (TOCC). Through comparing the predicting results, the following conclusions are drawn: (1) the accuracy of predicting by using the three ensemble models constructed is better than the corresponding prediction results by using the five sub-models; (2) the Random Forest ensemble model constructed based on the bagging method has the best prediction accuracy among the three ensemble models; and (3) in terms of the five sub-models, the prediction accuracy of LR is better than that of the other four models.

A hybrid random forests and artificial neural networks bagging ensemble for landslide susceptibility modelling

In this paper, an original methodology for landslide susceptibility mapping (LSM) is presented. It consists of bagging ensembles of artificial neural networks (ANNs) and random forests (RFs), and hybrid bagging ensembles of these models. It is applied on the area of the Itajaí-Açu river valley. In December 2020, there was an extreme rainfall in the region, which triggered landslides. The RF ensemble presented slightly higher accuracy (0.941) than the ANN ensemble (0.940), but the ANN ensemble had a more balanced relation between sensitivity (0.966) and specificity (0.915) than the RF ensemble (specificity = 0.992, sensitivity = 0.891). The mixed ANN-RF ensemble presented the higher accuracy of all (0.950), and a good balance between sensitivity (0.948) and specificity (0.951), being considered the best model within those analyzed. The hybrid ensemble, together with classification threshold adjustment, removed discrepancies on the maps between both models by attenuating them.

Integrated cloud computing and cost effective modelling to delineate the ecological corridors for Spectacled bears (Tremarctos ornatus) in the rural territories of the Peruvian Amazon

Spectacled bears (SB) (Tremarctos ornatus) are the only bear species native to South America. This particular bear is the single species of its genus, and it is listed as vulnerable according to the IUCN red list. A critical SB conservation habitat is in the rural territories of the Peruvian Amazon, where anthropogenic land-use changes and landscape fragmentation threaten SB habitats. The following questions arise in this context: How much has land-use changed? How to design the establishment of ecological corridors (ECs) to support the conservation of SB?. We investigated the temporal land use and land cover changes for last 30 years (1990–2020) for a better projection of the ECs and to quantify the temporal landscape metrics. Furthermore, we integrated cloud computing, machine learning models with cost-effective techniques to delineate the ECs for SB within the rural territories. Ensemble Random Forest model associated with Google Earth Engine (GEE) was used to develop four land use and land cover (LULC) maps (for the years 1990, 2000, 2010 and 2020). The least cost path (LCP) model based on Dijkstra’s shortest path algorithm was assembled based on six variables (altitude; slope; distance to roads; distance to population centers; land use map; inventory map of SB). Then, we calculated the ECs based on the multidirectional origin-destination points, we found that forest patches increased by 57% between 1990 and 2020. Results showed statistically significant agreement (R 2 = 0.47; p < 0.05) between cost/ha* and percentage of forest cover. We observed that the higher the forest cover, the better the connectivity and the lower the cost of mobilization in the ECs. Our study outcomes validated through the images obtained from trap cameras that confirms that delineated routs for SB movements. The proposed model can be adopted for other parts of the global forest including other species of interest. To formulate a sustainable conservation action plan, we provided five recommendations that will support conservation practices, design cost-effective ECs for policy makers.

Prediction of spontaneous imbibition in porous media using deep and ensemble learning techniques

Spontaneous imbibition (SI), which is a process of displacing a nonwetting fluid by a wetting fluid in porous media, is of critical importance to hydrocarbon recovery from fractured reservoirs. In the present study, we utilize deep and ensemble learning techniques to predict SI recovery in porous media under different boundary conditions including All-Faces-Open (AFO), One-End-Open (OEO), Two-Ends-Open (TEO), and Two-Ends-Closed (TEC). An extensive experimental dataset reported in literature representing a multiplicity of non-wetting fluid recovery-time curves was used in our analysis. The prepared dataset was used to learn diverse ensemble and deep learning algorithms of Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Voting Regressor (VR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The training procedure provided us with robust models linking the SI recovery to the absolute permeability (k), porosity (ϕ), characteristic length (Lc), interfacial tension (σ), wetting-phase viscosity (μw), non-wetting-phase viscosity (μnw), and imbibition time (t). To evaluate and validate the models’ prediction, we used two well-established approaches: (i) 10-fold cross-validation and (ii) predicting the SI behavior of a set of unseen data excluded from the model training. Our results illustrate an excellent performance of deep and ensemble learning techniques for prediction of SI with the test RMSE values of 4.642, 4.088, 4.524, 3.933, 3.875, 3.975, 4.513, and 4.807 percent for RF, GBM, XGBoost, LightGBM, VR, CNN, LSTM, and GRU models, respectively. The models have significant benefits in terms of accuracy and generality. Furthermore, they alleviate the sophistications associated with tuning the traditional correlation functions. The findings of this study can pave the road toward a more comprehensive characterization of fluid flow in porous materials which is important to a wide range of environmental and energy-related challenges such as contaminant transport, soil remediation, and enhanced oil recovery.

Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

Gully erosion is one of the important problems creating barrier to agricultural development. The present research used the radial basis function neural network (RBFnn) and its ensemble with random sub-space (RSS) and rotation forest (RTF) ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility (GES) in Hinglo river basin. 120 gullies were marked and grouped into four-fold. A total of 23 factors including topographical, hydrological, lithological, and soil physio-chemical properties were effectively used. GES maps were built by RBFnn, RSS-RBFnn, and RTF-RBFnn models. The very high susceptibility zone of RBFnn, RTF-RBFnn and RSS-RBFnn models covered 6.75%, 6.72% and 6.57% in Fold-1, 6.21%, 6.10% and 6.09% in Fold-2, 6.26%, 6.13% and 6.05% in Fold-3 and 7%, 6.975% and 6.42% in Fold-4 of the basin. Receiver operating characteristics (ROC) curve and statistical techniques such as mean-absolute-error (MAE), root-mean-absolute-error (RMSE) and relative gully density area (R-index) methods were used for evaluating the GES maps. The results of the ROC, MAE, RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency. The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.

An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study.

Diabetes is a long-lasting disease triggered by expanded sugar levels in human blood and can affect various organs if left untreated. It contributes to heart disease, kidney issues, damaged nerves, damaged blood vessels, and blindness. Timely disease prediction can save precious lives and enable healthcare advisors to take care of the conditions. Most diabetic patients know little about the risk factors they face before diagnosis. Nowadays, hospitals deploy basic information systems, which generate vast amounts of data that cannot be converted into proper/useful information and cannot be used to support decision making for clinical purposes. There are different automated techniques available for the earlier prediction of disease. Ensemble learning is a data analysis technique that combines multiple techniques into a single optimal predictive system to evaluate bias and variation, and to improve predictions. Diabetes data, which included 17 variables, were gathered from the UCI repository of various datasets. The predictive models used in this study include AdaBoost, Bagging, and Random Forest, to compare the precision, recall, classification accuracy, and F1-score. Finally, the Random Forest Ensemble Method had the best accuracy (97%), whereas the AdaBoost and Bagging algorithms had lower accuracy, precision, recall, and F1-scores.

Augmentation of AI-based Process for Blood Doping Discovery in Sports using Random Forest Ensembles with LightGBM

Metadata is an exploration of the given data. It organizes the data by grouping the collected information on a particular structure for easy understanding. Metadata reduces the computational burden on data mining algorithms by keeping an organized record. Data that are related to healthcare application requires serious attention on privacy concern and therefore such information are encrypted in most cases. Processing of encrypted data is difficult, and it may lead to estimate the prediction with a faulty output. Hence, a blockchain based data securing system is proposed in the work for securing the data that are transmitted from a ubiquitous computing device. The paper also incorporates the proposed work with a whale optimization algorithm for reducing the execution time required on the blockchain based data storage and retrieval process.

Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection.

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

Real-Time Data-Driven Approach for Prediction and Correction of Electrode Array Trajectory in Cochlear Implantation

Cochlear implants provide hearing perception to people with severe to profound hearing loss. The electrode array (EA) inserted during the surgery directly stimulates the hearing nerve, bypassing the acoustic hearing system. The complications during the EA insertion in the inner ear may cause trauma leading to infection, residual hearing loss, and poor speech perception. This work aims to reduce the trauma induced during electrode array insertion process by carefully designing a sensing method, an actuation system, and data-driven control strategy to guide electrode array in scala tympani. Due to limited intra-operative feedback during the insertion process, complex bipolar electrical impedance is used as a sensing element to guide EA in real time. An automated actuation system with three degrees of freedom was used along with a complex impedance meter to record impedance of consecutive electrodes. Prediction of EA direction (medial, middle, and lateral) was carried out by an ensemble of random forest, shallow neural network, and k-nearest neighbour in an offline setting with an accuracy of 86.86%. The trained ensemble was then utilized in vitro for prediction and correction of EA direction in real time in the straight path with an accuracy of 80%. Such a real-time system also has application in other electrode implants and needle and catheter insertion guidance.

Feature ranking for semi-supervised learning

The data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high-dimensional space, where not all examples have labels provided. For example, when investigating the toxicity of chemical compounds, there are many compounds available that can be described with information-rich high-dimensional representations, but not all of the compounds have information on their toxicity. To address these challenges, we propose methods for semi-supervised learning (SSL) of feature rankings. The feature rankings are learned in the context of classification and regression, as well as in the context of structured output prediction (multi-label classification, MLC, hierarchical multi-label classification, HMLC and multi-target regression, MTR) tasks. This is the first work that treats the task of feature ranking uniformly across various tasks of semi-supervised structured output prediction. To the best of our knowledge, it is also the first work on SSL of feature rankings for the tasks of HMLC and MTR. More specifically, we propose two approaches—based on predictive clustering tree ensembles and the Relief family of algorithms—and evaluate their performance across 38 benchmark datasets. The extensive evaluation reveals that rankings based on Random Forest ensembles perform the best for classification tasks (incl. MLC and HMLC tasks) and are the fastest for all tasks, while ensembles based on extremely randomized trees work best for the regression tasks. Semi-supervised feature rankings outperform their supervised counterparts across the majority of datasets for all of the different tasks, showing the benefit of using unlabeled in addition to labeled data.

Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL Intrusion Attack Detection Model.

The exponential rise in advanced software computing and low-cost hardware has broadened the horizon for the Internet of Medical Things (IoMT), interoperable e-Healthcare systems serving varied purposes including electronic healthcare records (EHRs) and telemedicine. However, being heterogeneous and dynamic in nature, their database security remains a challenge forever. Numerous intrusion attacks including bot-attack and malware have confined major classical databases towards e-Healthcare. Despite the robustness of NoSQL over the structured query language databases, the dynamic data nature over a heterogeneous environment makes it vulnerable to intrusion attacks, especially over interoperable e-Healthcare systems. Considering these challenges, this work proposed a first of its kind semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model for interoperable e-Healthcare systems. This work assessed the efficacy of the different semantic feature-extraction methods like Word2Vec, Continuous Bag of Words, N-Skip Gram (SKG), Count Vectorizer, TF-IDF, and GLOVE towards NoSQL-IA prediction. Subsequently, to minimize computational exhaustion, different feature selection methods including Wilcoxon Rank Sum Test (WRST), significant predictor test, principal component analysis, Select K-Best, and variance threshold feature selection algorithms were employed. To alleviate the data imbalance problem, it applied different resampling methods including upsampling, downsampling, and synthetic minority oversampling technique (SMOTE) over the selected features. Later, Min–Max normalization was performed over the input feature vectors to alleviate any possibility of overfitting. Towards NoSQL-IA prediction, different machine learning methods like Multinomial Naïve Bayes, decision tree, logistic regression, support vector machine, k-NN, AdaBoost, Extra Tree Classifier, random forest ensemble, and XG-Boost were applied, which classified each input query as the regular query or the NoSQL-IA attack query. The depth performance assessment revealed that the use of Word2Vec features SKG in sync with VTFS feature selection and SMOTE resampling processed with the bootstrapped random forest classifier can provide the best performance in terms of high accuracy (98.86%), F-Measure (0.974), and area under the curve (AUC) (0.981), thus enabling it suitable for interoperable e-Healthcare database security.