Performance Of Machine Learning Techniques Research Articles

Leverage machine learning to identify key measures in hospital operations management: a retrospective study to explore feasibility and performance of four common algorithms

BackgroundMeasures in operations management are pivotal for monitoring and assessing various aspects of hospital performance. Existing literature highlights the importance of regularly updating key management measures to reflect changing trends and organizational goals. Advancements in machine learning (ML) have presented promising opportunities for enhancing the process of updating operations management measures. However, their specific application and performance remain relatively unexplored. We aimed to investigate the feasibility and effectiveness of using common ML techniques to identify and update key measures in hospital operations management.MethodsHistorical data on 43 measures on financial balance and quality of care under 4 categories were retrieved from the BI system of a regional health system in Central China. The dataset included 17 surgical and 15 non-surgical departments over 48 months. Four common ML techniques, linear models (LM), random forest (RF), partial least squares (PLS), and neural networks (NN), were used to identify the most important measures. Ordinary least square was employed to investigate the impact of the top 10 measures. A ground truth validation compared the ML-identified key measures against the humanly decided strategic measures from annual meeting minutes.ResultsFor financial balancing, inpatient treatment revenue was an important measure in 3/4 years, followed by equipment depreciation costs. The measures identified using the same technique differed between years, though RF and PLS yielded relatively consistent results. For quality of care, none of the ML-identified measures repeated over the years. Those consistently important over four years differed almost entirely among four techniques. On ground truth validation, the 2016–2019 ML-identified measures were among the humanly identified measures, with the exception of equipment depreciation from the 2019 dataset. All the ML-identified measures for quality of care failed to coincide with the humanly decided measures.ConclusionsUsing ML to identify key hospital operational measures is viable but performance of ML techniques vary considerably. RF performs best among the four techniques in identifying key measures in financial balance. None of the ML techniques seem effective for identifying quality of care measures. ML is suggested as a decision support tool to remind and inspire decision-makers in certain aspects of hospital operations management.

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning. This retrospective cohort study was conducted with 823 living kidney donors between 2009 and 2020. The dataset was randomly split into training (80%) and test sets (20%). The main outcome was the post-donation estimated glomerular filtration rate (eGFR) 12months after nephrectomy. We compared the performance of machine learning techniques, traditional regression models, and models from previous studies. The best-performing model was selected based on the mean absolute error (MAE) and root mean square error (RMSE). The mean age of the participants was 45.2 ± 12.3years, and 48.4% were males. The mean pre-donation and post-donation eGFRs were 101.3 ± 13.0and 68.8 ± 12.7mL/min/1.73m2, respectively. The XGBoost model with the eGFR, age, serum creatinine, 24-h urine creatinine, 24-h urine sodium, creatinine clearance, cystatin C, cystatin C-based eGFR, computed tomography volume of the remaining kidney/body weight, normalized GFR of the remaining kidney measured through a diethylenetriaminepentaacetic acid scan, and sex, showed the best performance with a mean absolute error of 6.23 and root mean square error of 8.06. An easy-to-use web application titled Kidney Donation with Nephrologic Intelligence (KDNI) was developed. The prediction model using XGBoost accurately predicted the post-donation eGFR after living kidney donation. This model can be applied in clinical practice using KDNI, the developed web application.

Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application

Credit risk arises as a result of the failure of the loans given by banks to the customers to fulfill their obligations at the end of the specified term. Technological advances allow the use of machine learning methods in various sectors. These methods aim to facilitate the identification of customers at risk with the system adapted to the creditworthiness processes of banks. For this purpose, in order to make the most appropriate evaluation in the lending process of banks, re-sampling techniques to eliminate the problem of class imbalance encountered in unbalanced data sets were made balanced and their effects on machine learning were investigated. During the implementation phase, German, Australian and HMEQ credit data sets were used. Different machine learning classification methods such as Logistic Regression (LR), K-Narest Neighbor (KNN), Naive Bayes (NB), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Extremely Randomized Trees, Hard and Soft Voting were used to detect risky customers. The problem of class imbalance was balanced with resampling and hybrid techniques such as Random Oversampling (ROS), Random Undersampling (RUS), Balanced Bagging Classifier (BBC), SMOTE-Tomek Links and SMOTE-ENN. In this context, the performances of three different data sets were examined in four different scenarios. As a result of the study, the hybrid method, in which oversampling and undersampling methods are used together for the class balancing problem, showed the best classification performance among machine learning techniques.

The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

In the field of data engineering in machine learning (ML), a crucial component is the process of scaling, normalization, and standardization. This process involves transforming data to make it more compatible with modeling techniques. In particular, this transformation is essential to ensure the suitability of the data for subsequent analysis. Despite the application of many conventional and relatively new approaches to ML, there remains a conspicuous lack of research, particularly in the geotechnical discipline. In this study, ML-based prediction models (i.e., RF, SVR, Cubist, and SGB) were developed to estimate the undrained shear strength (UDSS) of cohesive soil from the perspective of a wide range of data-scaling and transformation methods. Therefore, this work presents a novel ML framework based on data engineering approaches and the Cubist regression method to predict the UDSS of cohesive soil. A dataset including six different features and one target variable were used for building prediction models. The performance of ML models was examined considering the impact of the data pre-processing issue. For that purpose, data scaling and transformation methods, namely Range, Z-Score, Log Transformation, Box-Cox, and Yeo-Johnson, were used to generate the models. The results were then systematically compared using different sampling ratios to understand how model performance varies as various data scaling/transformation methods and ML algorithms were combined. It was observed that data transformation or data sampling methods had considerable or limited effects on the UDSS model performance depending on the algorithm type and the sampling ratio. Compared to RF, SVR, and SGB models, Cubist models provided higher performance metrics after applying the data pre-processing steps. The Box-Cox transformed Cubist model yielded the best prediction performance among the other models with an R2 of 0.87 for the 90% training set. Also, the UDSS prediction model generally yielded the best performance metrics when it was used with the transformed-based models (i.e., Box-Cox, Log, and Yeo-Johnson) than that of scaled-based (i.e., Range and Z-Score) models. The results show that the Cubist model has a higher potential for UDSS prediction, and data pre-processing methods have impacts on the predictive capacity of the evaluated regression models.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

Prediction of Cochlear Implant Fitting by Machine Learning Techniques.

This study aimed to evaluate the differences in electrically evoked compound action potential (ECAP) thresholds and postoperative mapping current (T) levels between electrode types after cochlear implantation, the correlation between ECAP thresholds and T levels, and the performance of machine learning techniques in predicting postoperative T levels. Retrospective case review. Tertiary hospital. We reviewed the charts of 124 ears of children with severe-to-profound hearing loss who had undergone cochlear implantation. We compared ECAP thresholds and T levels from different electrodes, calculated correlations between ECAP thresholds and T levels, and created five prediction models of T levels at switch-on and 6 months after surgery. The accuracy of prediction in postoperative mapping current (T) levels. The ECAP thresholds of the slim modiolar electrodes were significantly lower than those of the straight electrodes on the apical side. However, there was no significant difference in the neural response telemetry thresholds between the two electrodes on the basal side. Lasso regression achieved the most accurate prediction of T levels at switch-on, and the random forest algorithm achieved the most accurate prediction of T levels 6 months after surgery in this dataset. Machine learning techniques could be useful for accurately predicting postoperative T levels after cochlear implantation in children.

Textual similarity for legal precedents discovery: Assessing the performance of machine learning techniques in an administrative court

The importance of legal precedents in ensuring consistent jurisprudence is undisputed. Particularly in jurisdictions following the Common law, but even in Civil law systems, uniformity in case law requires adherence to precedents. However, with the growing volume of cases, manual identification becomes a bottleneck, prompting the need for automation. Leveraging the capabilities of natural language processing (NLP) and machine learning (ML), our study delves into the potential of automation in identifying similar cases indicative of precedents. Drawing from a unique, substantial dataset of legal cases from an administrative court in Brazil, we extensively evaluated over one hundred combinations of document representations and text vectorizations. Contrary to earlier studies that relied on minimal validation samples, ours employed a statistically significant sample vetted by legal experts. Our findings reveal that models focusing on granular text representations perform optimally, especially when extracting concepts and relations. Notably, while intricate models may not always guarantee superior outcomes, the importance of refining textual features cannot be understated. These findings pave the way for creating efficient decision support systems in judicial contexts and set a direction for future research aiming to integrate technology in legal decision-making.

Machine learning techniques in breast cancer preventive diagnosis: a review

Breast cancer (BC) is known as the most prevalent form of cancer among women. Recent research has demonstrated the potential of Machine Learning (ML) techniques in predicting the five-year BC risk using personal health data. Support Vector Machine (SVM), Random Forest, K-NN (K-Nearest Neighbour), Naive Bayes, Neural Network, Decision Tree (DT), Logistic Regression (LR), Discriminant Analysis, and their variants are commonly employed in ML for BC analysis. This study investigates the factors influencing the performance of ML techniques in the domain of BC prevention, with a focus on dataset size and feature selection. The study's goal is to examine the effect of dataset cardinality, feature selection, and model selection on analytical performance in terms of Accuracy and Area Under the Curve (AUC). To this aim, 3917 papers were automatically selected from Scopus and PubMed, considering all publications from the previous 5 years, and, after inclusion and exclusion criteria, 54 articles were selected for the analysis. Our findings highlight how a good cardinality of the dataset and effective feature selection have a higher impact on the model's performance than the selected model, as corroborated by one of the studies, which gets extremely good results with all of the models employed.

Application of the performance of machine learning techniques as support in the prediction of school dropout

This article presents a study, intending to design a model with 90% reliability, which helps in the prediction of school dropouts in higher and secondary education institutions, implementing machine learning techniques. The collection of information was carried out with open data from the 2015 Intercensal Survey and the 2010 and 2020 Population and Housing censuses carried out by the National Institute of Statistics and Geography, which contain information about the inhabitants and homes. in the 32 federal entities of Mexico. The data were homologated and twenty variables were selected, based on the correlation. After cleaning the data, there was a sample of 1,080,782 records in total. Supervised learning was used to create the model, automating data processing with training and testing, applying the following techniques, Artificial Neural Networks, Support Vector Machines, Linear Ridge and Lasso Regression, Bayesian Optimization, Random Forest, the first two with a reliability greater than 99% and the last with 91%.

Machine learning based software effort estimation using development-centric features for crowdsourcing platform

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.

Evaluation of machine learning techniques for heart disease prediction using multi-criteria decision making

Globally, heart disease is the primary cause of death. Early detection of this disease enables cardiologists to make more accurate judgments regarding the health of their patients. Due to machine learning’s ability to identify patterns in data, its use in the medical industry has increased. Many heart disease prediction models have been developed by various researchers utilizing machine learning techniques (MLTs). The performance of MLTs on heart disease prediction may vary for different accuracy measures. Thus, the choice of the appropriate machine-learning technique for heart disease prediction is a challenging task. This paper proposes a multi-criteria decision-making (MCDM)-based method to evaluate the MLTs for heart disease prediction considering various performance measures taken into account altogether. The proposed approach uses the concept of a combined compromise solution (CoCoSo)- an MCDM method. For validation of the proposed approach, an experimental study was conducted to evaluate the performance of fifteen machine learning techniques for predicting heart disease over three heart disease datasets considering six performance measures taken into account altogether. Results show that the logistic regression and support vector machine are recommended as the most suitable MLTs for heart disease prediction modeling with respect to six performance measures considered simultaneously.

Enhancing Predictive Quality in HVOF Coating Technology: A Comparative Analysis of Machine Learning Techniques

This paper presents a comparative analysis of the performance of machine learning techniques for enhancing predictive quality in thermal spray coating technology, with a focus on the high velocity oxygen fuel (HVOF) process. The study aims for accurately predicting coating properties, which are pivotal in determining coating quality in HVOF applications. By investigating five influential factors, including the fuel-to-oxygen ratio and coating velocity, this research was designed to improve the understanding and control of these properties. Through advanced statistical design of experiments, correlations between these factors and key coating properties are established. Six machine learning models are trained and evaluated to assess their predictive capabilities for coating properties. The obtained results provide valuable insights into the HVOF coating process and highlight the potential of predictive model-based techniques for accurately predicting such properties. Furthermore, this research offers guidance for selecting an effective machine learning technique in the context of thermal spray technology.

Retracted: Analyzing the Performance of Machine Learning Techniques in Disease Prediction

Retracted: Analyzing the Performance of Machine Learning Techniques in Disease Prediction

Evaluating the impact of sampling designs on the performance of machine learning techniques for land use land cover classification using Sentinel-2 data

ABSTRACT In today’s world, by integrating remote sensing technology and modern state-of-the-art machine learning techniques, obtaining Land Use Land Cover (LULC) maps has become easier in comparison to traditional manual methods. The performance of a Machine Learning classifier is influenced by various factors. The objective of this study is to evaluate the impact of sampling design in rough complex terrain located in the Northern Himalayan region in Uttarakhand state, India, where reference data is often limited due to the geographical characteristics of the study area. Three sampling design strategies have been incorporated in this study, namely, stratified random sampling with a proportional number of samples (SRS)proportional, stratified random sampling with an equal number of samples (SRS)equivalent and stratified systematic sampling with an equal number of samples with a minimum distance of 10 m between the consecutive samples (SSS)D = 10 m for the LULC classification. In this study, Sentinel-2 data of 10 m spatial resolution for the study area of Dehradun district, Uttarakhand, India, has been selected. The following conclusions can be drawn from the results of this study (i) (SRS)proportional achieved the highest Overall Accuracy (OA) among all the three sampling techniques. The OA and kappa score (ka) using (SRS)proportional are OA = 90.25 and ka = 0.874 by Random Forest, OA = 88.84 and ka = 0.856 by Support Vector Machine and k Nearest Neighbours (kNN) obtained OA = 87.72 and ka = 0.842, respectively. (ii) It was found that in the case of (SRS)proportional, the majority classes like the deciduous forest, evergreen forest and cropland achieved higher recall and precision values in comparison to those obtained from the other two sampling strategies, i.e. (SRS)equivalent and (SSS)D = 10 m. (iii) The results showed that while switching from (SRS)proportional to (SRS)equivalent or from (SRS)proportional to (SSS)D = 10 m, there was a slight reduction in the precision and recall values for the majority classes and a slight increase for a few of the minority classes.

Control of chaotic systems through reservoir computing

Chaos is an important dynamic feature, which generally occurs in deterministic and stochastic nonlinear systems and is an inherent characteristic that is ubiquitous. Many difficulties have been solved and new research perspectives have been provided in many fields. The control of chaos is another problem that has been studied. In recent years, a recurrent neural network has emerged, which is widely used to solve many problems in nonlinear dynamics and has fast and accurate computational speed. In this paper, we employ reservoir computing to control chaos in dynamic systems. The results show that the reservoir calculation algorithm with a control term can control the chaotic phenomenon in a dynamic system. Meanwhile, the method is applicable to dynamic systems with random noise. In addition, we investigate the problem of different values for neurons and leakage rates in the algorithm. The findings indicate that the performance of machine learning techniques can be improved by appropriately constructing neural networks.

Predicating depression on Twitter using hybrid model BiLSTM-XGBOOST

Nowadays, depression is a common mental illness. Failure to recognize depression early or guarantee that a depressed individual receives prompt counseling can lead to serious issues. Social media allow us to monitor people's thoughts, daily activities, and emotions, including persons with mental illnesses. This study suggested novel hybrid models that combine one of the deep learning techniques with one of the machine learning approaches. This paper used a dataset from the Kaggle website to predict depression. Two deep learning techniques were chosen to conduct the experiments: bidirectional long short-term memory (Bi-LSTM), and convolutional neural network (CNN). Three machine learning techniques were also selected, which are support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBOOST). Deep learning methods were applied to extract important features from input data and training, and then machine learning was utilized to predict the class. The performance of the hybrid models was compared against that of five single models. The results showed that Bi-LSTM-XGBOOST is better than single models and achieve the highest performance, with 94% for all evaluation metrics. The proposed model can improve the performance of machine learning techniques and increase the detection rate of depression.

Determination of Johnson-Champoux-Allard model parameters with machine learning techniques

Acoustic modelling of materials is a key aspect to perform reliable predictions of untested configurations and to implement optimal strategies, especially nowadays, due to the development of 3D printing techniques allowing to generate deterministic microstructures in porous materials. However, this modelling is not straightforward due to the number of parameters characterizing porous materials. Each one of these parameters can be estimated experimentally with its own setup and procedure, not always easy to execute. On the other hand, the use of inverse methods for the estimation of parameters relies on physical approximations and requires experimental procedures that are strongly sensitive to boundary conditions. Consequently, experimental determination of the parameters of porous materials may become financially expensive and very time consuming. The purpose of this work is to investigate the performances of machine learning techniques in determining the parameters of Johnson-Champoux-Allard model, in order to understand whether missing values can be predicted just relying on a set of already performed experimental characterizations when not all the experimental setups are available. In particular, Gaussian processes are used on a training set describing porous materials in terms of Johnson-Champoux-Allard parameters and the values of acoustic indices at fixed frequencies.

Mapping Soil Organic Matter Using Different Modeling Techniques in the Dryland Agroecosystem of Huang-Huai-Hai Plain, Eastern China

Accurately mapping the spatial distribution and variation of soil organic matter (SOM) is of great significance for guiding regional soil management. However, the applicability and prediction performance of machine learning techniques in dryland agroecosystems still needs to be further studied. In this study, we collected a total of 733 topsoil samples from the farmland in Xiao County, Anhui Province, which is a typical dryland agroecosystem in the Huang-Huai-Hai Plain. Then, the environmental covariates were selected, and the ordinary kriging (OK), multiple linear stepwise regression (MLR), regression kriging (RK), radial basis function neural network (RBFNN), and random forest (RF) models were conducted to map the SOM content, and the optimal model was ascertained. The results demonstrated that the alkali-hydrolyzable nitrogen (26.11%), available potassium (17.73%), mean annual precipitation (13.26%), and pH (11.80%) were the main controlling factors affecting the spatial distribution of SOM in the study area. Meanwhile, the introduction of environmental covariates can effectively improve the SOM prediction accuracy, and the RF model (R2 = 0.48, MAE = 2.38 g kg−1, MRE = 12.99%, RMSE = 3.14 g kg−1) has a better performance than the RFBNN, MLR, RK, and OK methods. Although there are local differences in the spatial distribution of SOM predicted by the five methods, the overall spatial distribution of SOM was characterized by the low concentration area (13.44–20.00 g kg−1) distributed in the central and northwest of study area, and the high concentration area (24.00–28.95 g kg−1) distributed in the southeast. Overall, our study demonstrated that machine learning-based models could accurately predict the SOM content in dryland agroecosystem, and the produced maps function as baseline maps for sustainable agricultural management.

Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

This paper presents a study on the performance of Machine Learning techniques for the task of determining each of the electronics systems connected within the same electrical network, in the Internet of Smart Grid Things (IoSGT) architecture. This is regarded as an ecosystem which has cutting-edge technologies that work together to enable advanced SG applications, and run in core/edge cloud datacenters connected to an underlying IoT network infrastructure. This identification was carried out by analyzing traditional energy measures (i.e., voltage, current and power) through ML-based techniques. The analysis of regular energy measurements is required as a means of ensuring compatibility with legacy /smart meters, without the need to exchange them for new ones or make updates. The data was obtained by using a smart meter built by our group, and was processed and validated in the IoSGT edge-cloud.

Mitigating anomalous electricity consumption in smart cities using an AI‐based stacked‐generalization technique

AbstractEnergy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non‐technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the main issue in maintaining grid stability and cause revenue loss to the utility. The automatic metering infrastructure (AMI) system has reduced grid instability but it has opened up new ways for NTLs in the form of different cyber‐physical theft attacks (CPTA). Machine learning (ML) techniques can be used to detect and minimize CPTA. However, they have certain limitations and cannot capture the energy consumption patterns (ECPs) of all the users, which decreases the performance of ML techniques in detecting malicious users. In this paper, we propose a novel ML‐based stacked generalization method for the cyber‐physical theft issue in the smart grid. The original data obtained from the grid is preprocessed to improve model training and processing. This includes NaN‐imputation, normalization, outliers' capping, support vector machine‐synthetic minority oversampling technique (SVM‐SMOTE) balancing, and principal component analysis (PCA) based data reduction techniques. The pre‐processed dataset is provided to the ML models light gradient boosting (LGB), extra trees (ET), extreme gradient boosting (XGBoost), and random forest (RF), to accurately capture all consumers' overall ECP. The predictions from these base models are fed to a meta‐classifier multi‐layer perceptron (MLP). The MLP combines the learning capability of all the base models and gives an improved final prediction. The proposed structure is implemented and verified on the publicly available real‐time large dataset of the State Grid Corporation of China (SGCC). The proposed model outperformed the individual base classifiers and the existing research in terms of CPTA detection with false positive rate (FPR), false negative rate (FNR), F1‐score, and accuracy values of 0.72%, 2.05%, 97.6%, and 97.69%, respectively.