Performance Of Machine Learning Models Research Articles

Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach.

Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model's interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Application of the DRASTIC-LU/LC method combined with machine learning models to assess and predict the vulnerability of the Rmel aquifer (Northwest, Morocco)

The Rmel aquifer, located in the Tangier-Tetouan-Al Hoceima region of northwest Morocco, covers approximately 240 km2 and faces increasing pollution threats due to population growth and economic development. This study assesses aquifer vulnerability to pollution, and compares the performance of various machine learning models integrated with the DRASTIC-LU/LC method. The research used a dataset of 52 water samples analyzed for nitrate concentrations, considering eight factors influencing vulnerability: aquifer depth, net recharge, aquifer lithology, soil texture, topography, vadose zone impact, hydraulic conductivity, and land use. An information gain test was applied to evaluate the importance of these factors. Four machine learning algorithms were used with the DRASTIC-LU/LC method: multilayer perceptron (MLP), the bagging algorithm (BA), K-nearest neighbors (KNN), and extremely randomized trees (ERT). Model performance was assessed via the area under the ROC curve (ROC-AUC) to measure accuracy. The ERT model combined with DRASTIC-LU/LC achieved the highest accuracy (AUC = 0.929), followed by BA (AUC = 0.925), MLP (AUC = 0.852), and KNN (AUC = 0.787). In comparison, the original DRASTIC-LU/LC model had an AUC of 0.530. The results highlight significant vulnerability variation across the Rmel aquifer, with high to very high levels in the southern and northwestern regions, and moderate to low levels in the northeast and central areas. Vulnerability maps were validated by comparing the observed nitrate concentrations in the water samples, confirming model accuracy. Groundwater depth, net recharge, and hydraulic conductivity were identified as the most significant factors influencing vulnerability. This study demonstrates the effectiveness of integrating machine learning models with the DRASTIC-LU/LC method for accurate aquifer vulnerability assessment, offering valuable tools for public policy and groundwater management.

Parametric optimization and comparative study of machine learning and deep learning algorithms for breast cancer diagnosis.

Breast Cancer is the leading form of cancer found in women and a major cause of increased mortality rates among them. However, manual diagnosis of the disease is time-consuming and often limited by the availability of screening systems. Thus, there is a pressing need for an automatic diagnosis system that can quickly detect cancer in its early stages. Data mining and machine learning techniques have emerged as valuable tools in developing such a system. In this study we investigated the performance of several machine learning models on the Wisconsin Breast Cancer (original) dataset with a particular emphasis on finding which models perform the best for breast cancer diagnosis. The study also explores the contrast between the proposed ANN methodology and conventional machine learning techniques. The comparison between the methods employed in the current study and those utilized in earlier research on the Wisconsin Breast Cancer dataset is also compared. The findings of this study are in line with those of previous studies which also highlighted the efficacy of SVM, Decision Tree, CART, ANN, and ELM ANN for breast cancer detection. Several classifiers achieved high accuracy, precision and F1 scores for benign and malignant tumours, respectively. It is also found that models with hyperparameter adjustment performed better than those without and boosting methods like as XGBoost, Adaboost, and Gradient Boost consistently performed well across benign and malignant tumours. The study emphasizes the significance of hyperparameter tuning and the efficacy of boosting algorithms in addressing the complexity and nonlinearity of data. Using the Wisconsin Breast Cancer (original) dataset, a detailed summary of the current status of research on breast cancer diagnosis is provided.

Dual cross-linked magnetic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling

To achieve highly efficient and environmentally degradable adsorbents for Congo red (CR) removal, we synthesized a dual-network nanocomposite cryogel composed of gelatin/carboxymethyl cellulose, loaded with Fe3O4 nanoparticles. Gelatin and sodium carboxymethylcellulose were cross-linked using transglutaminase and calcium chloride, respectively. The cross-linking process enhanced the thermal stability of the composite cryogels. The CR adsorption process exhibited a better fit to the pseudo-second-order model and Langmuir model, with maximum adsorption capacity of 698.19 mg/g at pH of 7, temperature of 318 K, and initial CR concentration of 500 mg/L. Thermodynamic results indicated that the CR adsorption process was both spontaneous and endothermic. The performance of machine learning model showed that the Extreme Gradient Boosting model had the highest test determination coefficient (R2 = 0.9862) and the lowest root mean square error (RMSE = 10.3901 mg/g) among the 6 models. Feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that the initial concentration had the greatest influence on the model’s prediction of adsorption capacity. Density functional theory calculations indicated that there were active sites on the CR molecule that can undergo electrostatic interactions with the adsorbent. Thus, the synthesized cryogels demonstrate promising potential as adsorbents for dye removal from wastewater.

A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models

Agricultural water demand, groundwater extraction, surface water delivery and climate exhibit complex nonlinear relationships with groundwater storage in agricultural regions. As alternatives to computationally intensive physical models, data-driven machine learning methods are frequently employed as surrogates to capture these complex relationships, owing to their high computational efficiency. Inevitably, reliance on a single machine learning model may lead to underestimation of prediction uncertainty and potentially result in reduced accuracy. This paper presents a multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models. The key contributions of the research are threefold: 1) the development of a comprehensive input variable selection process considering the lag effects and driving mechanisms of groundwater level variations; 2) the implementation of an optimization-based hyperparameter tuning method to enhance the performance of individual machine learning models; and 3) the establishment of a multi-model integration framework based on a multi-layer nesting technique. This approach combines the outputs of multiple machine learning models to consolidate predictions and expand the hypothesis space. The effectiveness of the proposed framework is demonstrated through case studies in the Huaihe River Basin, a major agricultural region in China. The results show that the multi-layer nesting and integration approach outperforms the use of individual machine learning models, providing more accurate and reliable groundwater level predictions. This framework offers valuable insights for decision-makers and water resource managers, supporting sustainable groundwater management and addressing the challenges faced in agriculturally intensive areas.

Understanding the performance of machine learning models from data- to patient-level

Understanding the performance of machine learning models from data- to patient-level

Harmonizing sentiments: Analyzing user reviews of Spotify through sentiment analysis

This research investigates the sentiment of user reviews on Spotify, with a particular focus on the Indonesian market, leveraging advanced sentiment analysis techniques. We employed three prominent classification models—Naïve Bayes, Support Vector Machine (SVM), and Random Forest—to analyze a dataset of 14,296 user reviews extracted from the Google Play Store and App Store. These findings reveal that the SVM model achieved the highest performance, with an F1-score of 0.875 and an accuracy of 0.874, outperforming Naïve Bayes and Random Forest, which scored accuracies of 0.857 and 0.856 respectively. These results highlight not only the significance of this research which offers valuable contributions to the broader academic discourse on digital marketing, sentiment analysis, and consumer behavior. Additionally, it also showcases the robustness and superior performance of SVM and Random Forest in various sentiment analysis contexts. This study not only provides valuable insights for Spotify’s future development strategies but also contributes to the broader academic discourse on sentiment analysis and machine learning model performance in digital marketing. By highlighting the efficacy of specific models, this research underscores the importance of model selection in sentiment analysis, paving the way for more accurate and effective sentiment analysis applications in the music streaming industry.

Prognosis and risk factor assessment of patients with advanced lung cancer with low socioeconomic status: model development and validation

BackgroundLung cancer, a major global health concern, disproportionately impacts low socioeconomic status (SES) patients, who face suboptimal care and reduced survival. This study aimed to evaluate the prognostic performance of traditional Cox proportional hazards (CoxPH) regression and machine learning models, specifically Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), in patients with advanced lung cancer with low SES.DesignA retrospective study.MethodThe 949 patients with advanced lung cancer with low SES who entered the hospice ward of a tertiary hospital in Wuhan, China, from January 2012 to December 2021 were randomized into training and testing groups in a 3:1 ratio. CoxPH regression methods and four machine learning algorithms (DT, RF, SVM, and XGBoost) were used to construct prognostic risk prediction models.ResultsThe CoxPH regression-based nomogram demonstrated reliable predictive accuracy for survival at 60, 90, and 120 days. Among the machine learning models, XGBoost showed the best performance, whereas RF had the lowest accuracy at 60 days, DT at 90 days, and SVM at 120 days. Key predictors across all models included Karnofsky Performance Status (KPS) score, quality of life (QOL) score, and cough symptoms.ConclusionsCoxPH, DT, RF, SVM, and XGBoost models are effective in predicting mortality risk over 60–120 days in patients with advanced lung cancer with low SES. Monitoring KPS, QOL, and cough symptoms is crucial for identifying high-risk patients who may require intensified care. Clinicians should select models tailored to individual patient needs and preferences due to varying prediction accuracies.Reporting methodThis study was reported in strict compliance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline.Patient or public contributionNo patient or public contribution.

Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?

It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to the "curse of dimensionality". In tasks for which there is a vast amount of labeled data, Deep Networks seem to solve this issue with many layers and a non-Hebbian backpropagation algorithm. The brain, however, seems to be able to solve the problem with few layers. In this work, we hypothesize that this happens by using Hebbian learning. Actually, the Hebbian-like learning rule of Restricted Boltzmann Machines learns the input patterns asymmetrically. It exclusively learns the correlation between non-zero values and ignores the zeros, which represent the vast majority of the input dimensionality. By ignoring the zeros the "curse of dimensionality" problem can be avoided. To test our hypothesis, we generated several sparse datasets and compared the performance of a Restricted Boltzmann Machine classifier with some Backprop-trained networks. The experiments using these codes confirm our initial intuition as the Restricted Boltzmann Machine shows a good generalization performance, while the Neural Networks trained with the backpropagation algorithm overfit the training data.

Designing a reliable machine learning system for accurately estimating the ultimate condition of FRP-confined concrete

Precisely forecasting how concrete reinforced with fiber-reinforced polymers (FRP) responds under compression is essential for fine-tuning structural designs, ensuring constructions fulfill safety criteria, avoiding overdesigning, and consequently minimizing material expenses and environmental impact. Therefore, this study explores the viability of gradient boosting regression tree (GBRT), random forest (RF), artificial neural network-multilayer perceptron (ANNMLP) and artificial neural network-radial basis function (ANNRBF) in predicting the compressive behavior of fiber-reinforced polymer (FRP)-confined concrete at ultimate. The accuracy of the proposed machine learning approaches was evaluated by comparing them with several empirical models concerning three different measures, including root mean square errors (RMSE), mean absolute errors (MAE), and determination coefficient (R2). In this study, the evaluations were conducted using a substantial collection of axial compression test data involving 765 circular specimens of FRP-confined concrete assembled from published sources. The results indicate that the proposed GBRT algorithm considerably enhances the performance of machine learning models and empirical approaches for predicting strength ratio of confinement (f′cc/f′co) by an average improvement in RMSE as 17.3%, 0.65%, 66.81%, 46.12%, 46.31%, 46.87% and 69.94% compared to RF, ANNMLP, ANNRBF, and four applied empirical models, respectively. It is also found that the proposed ANNMLP algorithm exhibits notable superiority compared to other models in terms of reducing RMSE values as 9.67%, 11.29%, 75.11%, 68.83%, 73.64%, 69.49% and 83.74% compared to GBRT, RF, ANNRBF and four applied empirical models for predicting strain ratio of confinement (εcc/εco), respectively. The superior performance of the GBRT and ANNMLP compared to other methods in predicting the strength and strain ratio confinements is important in evaluating structural integrity, guaranteeing secure functionality, and streamlining engineering plans for effective utilization of FRP confinement in building projects.

Noninvasive brain stimulation during EEG improves machine learning classification in chronic stroke.

In individuals with chronic stroke and hemiparesis, noninvasive brain stimulation (NIBS) may be used as an adjunct to therapy for improving motor recovery. Specific states of movement during motor recovery are more responsive to brain stimulation than others, thus a system that could auto-detect movement state would be useful in correctly identifying the most effective stimulation periods. The aim of this study was to compare the performance of different machine learning models in classifying movement periods during EEG recordings of hemiparetic individuals receiving noninvasive brain stimulation. We hypothesized that transcranial direct current stimulation, a form of NIBS, would modulate brain recordings correlating with movement state and improve classification accuracies above those receiving sham stimulation. Electroencephalogram data were obtained from 10 participants with chronic stroke and 11 healthy individuals performing a motor task while undergoing transcranial direct current stimulation. Eight traditional machine learning algorithms and five ensemble methods were used to classify two movement states (a hold posture and an arm reaching movement) before, during and after stimulation. To minimize compute times, preprocessing and feature extraction were limited to z-score normalization and power binning into five frequency bands (delta through gamma). Classification of disease state produced significantly higher accuracies in the stimulation (versus sham) group at 78.9% (versus 55.6%, p < 0.000002). We observed significantly higher accuracies when classifying stimulation state in the chronic stroke group (77.6%) relative to healthy controls (64.1%, p < 0.0095). In the chronic stroke cohort, classification of hold versus reach was highest during the stimulation period (75.2%) as opposed to the pre- and post-stimulation periods. Linear discriminant analysis, logistic regression, and decision tree algorithms classified movement state most accurately in participants with chronic stroke during the stimulation period (76.1%). For the ensemble methods, the highest classification accuracy for hold versus reach was achieved using low gamma frequency (30-50 Hz) as a feature (74.5%), although this result did not achieve statistical significance. Machine learning algorithms demonstrated sufficiently high movement state classification accuracy in participants with chronic stroke performing functional tasks during noninvasive brain stimulation. tDCS improved disease state and movement state classification in participants with chronic stroke.

Multifunctional Image Enhancement Tool with Image Processing Methods

The success of image-based machine learning and deep learning algorithms depends on the amount and variety of data sets. The richness and heterogeneity of the dataset has a direct impact on the capacity of the model to generalize to new, previously unknown data. Obtaining a large number and variety of images is a very difficult and laborious process, especially for practical applications to be developed in areas such as health, industry and agriculture. If this difficulty cannot be overcome, the performance of machine learning models may decrease or may not reach the desired level at all. This problem may lead to problems such as poor performance or overfitting of the developed models. To solve this problem, data augmentation techniques are used to increase the amount and variety of data sets. These strategies help to provide the necessary data set for the model to learn more successfully. In this publication, we present an image improvement tool that is specifically developed for academics working with limited data sets. This tool uses a variety of data augmentation techniques, including rotation, zooming, panning, noise addition, and blurring, to expand the scale and diversity of available data. This application offers a realistic method for dealing with the issues of limited data availability in machine learning research.

Prediction on the spatial distribution of the seropositive rate of schistosomiasis in Hunan Province, China: a machine learning model integrated with the Kriging method.

Schistosomiasis remains a formidable challenge to global public health. This study aims to predict the spatial distribution of schistosomiasis seropositive rates in Hunan Province, pinpointing high-risk transmission areas and advocating for tailored control measures in low-endemic regions. Six machine learning models and their corresponding hybrid machine learning-Kriging models were employed to predict the seropositive rate. The optimal model was selected through internal and external validations to simulate the spatial distribution of seropositive rates. Our results showed that the hybrid machine learning-Kriging model demonstrated superior predictive performance compared to basic machine learning model and the Cubist-Kriging model emerged as the most optimal model for this study. The predictive map revealed elevated seropositive rates around Dongting Lake and its waterways with significant clustering, notably in the central and northern regions of Yiyang City and the northeastern areas of Changde City. The model identified gross domestic product, annual average wind speed and the nearest distance from the river as the top three predictors of seropositive rates, with annual average daytime surface temperature contributing the least. In conclusion, our research has revealed that integrating the Kriging method significantly enhances the predictive performance of machine learning models. We developed a Cubist-Kriging model with high predictive performance to forecast the spatial distribution of schistosomiasis seropositive rates. These findings provide valuable guidance for the precise prevention and control of schistosomiasis.

A novel infrared thermography image analysis for transformer condition monitoring

Electrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly maintenance and additional significant breakdowns. This research adopts the approach based on infrared thermography techniques (IRT) to keep eye on electrical transformers and detect their defects. Thermal images of the transformer were captured at two distinct operating states with an infrared camera. These images were then compiled into a dataset for further analysis. This method uses infrared imaging (IRT), along with feature analysis and machine learning, to identify issues in electrical transformers in a new way. To find the best performing machine learning model, different techniques are compared here in terms of their accuracy and stability. Two approaches are investigated for identifying features in thermography images. Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). Approach-2 utilized four deep learning techniques, such as MobileNetV2 (MNV2), InceptionV3 (InV3), DenseNet121(DN121), and our proposed modified VGG-16. Among all evaluated methods, the modified VGG-16 architecture achieved the highest level of dependability, demonstrating exceptional efficiency and accuracy in transformer condition monitoring and fault diagnosis.

Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants

BackgroundUnlike in adult and pediatric patients, the usefulness of lactate in preterm infants has not been thoroughly discussed. This study aimed to evaluate whether the lactate level in the first hours of life is an important factor associated with neonatal death in very-low-birth-weight (VLBW) preterm infants. MethodsElectronic medical records from a level 4 neonatal intensive care unit in South Korea were reviewed to obtain perinatal and neonatal outcomes. Data on lactate levels of preterm infants in the first 12 h of life were collected. Neonatal mortality and morbidities were compared based on lactate levels. Subsequently, machine-learning models incorporating 20 independent variables, both with and without lactate, were compared for model performances and feature importance of lactate for predicting in-hospital mortality in the applicable models. ResultsOne hundred and sixty-eight preterm infants were included. Death rates on days 7 and 30 of life (D30-mortality) were significantly higher in infants with high lactate levels (≥3rd interquartile range) than in those with lower levels (<3rd interquartile range). Though statistically insignificant, the overall in-hospital mortality was more than twice as high in the high lactate level group than in the lower lactate level group. Based on the machine learning results, Random Forest, Gradient Boosting, and LightGBM models all showed greater area under the curves when lactate was included. Lactate consistently ranked in the variables of top five feature importance, particularly showing the greatest value in the Gradient Boosting model. ConclusionLactate levels during the early hours of life may be an important factor associated with in-hospital death of preterm VLBW infants. Based on the enhanced performance of the above-mentioned machine learning models, lactate levels in the early postnatal period may add to assessing the clinical status and predicting the hospital course in this population.

Machine learning and statistical models to predict all-cause mortality in type 2 diabetes: Results from the UK Biobank study

AimsThis study aims to compare the performance of contemporary machine learning models with statistical models in predicting all-cause mortality in patients with type 2 diabetes mellitus and to develop a user-friendly mortality risk prediction tool. MethodsA prospective cohort study was conducted including 22,579 people with diabetes from the UK Biobank. Models evaluated include Cox proportional hazards, random survival forests (RSF), gradient boosting (GB) survival, DeepSurv, and DeepHit. ResultsOver a median follow-up period of 9 years, 2,665 patients died. Machine learning models outperformed the Cox model in the validation dataset, with C-index values of 0.72–0.73 vs. 0.71 for Cox (p < 0.01). Deep learning models, particularly DeepHit, demonstrated superior calibration and achieved lower Brier scores (0.09 vs. 0.10 for Cox, p < 0.05). An online prediction tool based on the DeepHit was developed for patient care: http://123.57.42.89:6006/. ConclusionsMachine learning models performed better than statistical models, highlighting the potential of machine learning techniques for predicting all-cause mortality risk and facilitating personalized healthcare management for individuals with diabetes.

A Comparative Analysis of Cloud-Based Healthcare Platforms through Effective Machine Learning Approaches

The integration of cloud computing and machine learning in healthcare platforms has revolutionized the delivery of medical services, offering scalable solutions for data storage, processing, and analysis. This study presents an overview of various cloud-based healthcare platforms, focusing on the effectiveness of machine learning approaches in enhancing patient care and operational efficiency, and compares the performance of different machine learning models employed in the platforms for diverse healthcare applications. The findings provide insights into the strengths and limitations of existing cloud-based healthcare solutions, guiding healthcare providers and policymakers in selecting optimal platforms for improved patient outcomes and resource utilization.

Improving class probability estimates in asymmetric health data classification: An experimental comparison of novel calibration methods

In the context of health data classification, imbalanced and asymmetric class distributions can significantly impact the performance of machine learning models. One critical aspect affected by these issues is the reliability of class probability estimates, which are crucial for informed decision-making in healthcare applications. Instead of predicting class values directly for a classification problem, it can be more convenient to predict the probability of an observation belonging to each possible class. This research aims to address the challenges posed by imbalanced and asymmetric responses in health data classification by evaluating the effectiveness of recent calibration methods in improving class probability estimates. We propose Beta calibration techniques and the Stratified Brier score and Jaccard's Score as novel calibration methods and evaluation metrics respectively. The experimental comparison involves implementing and assessing various calibration techniques to determine their impact on model performance and calibration accuracy of simulated and healthcare datasets with varying imbalance ratios. Our results show that the Beta calibration method consistently improved the classifiers' predictive ability. The findings of this study provide valuable insights into selecting the most suitable calibration method for enhancing class probability estimates in healthcare-related machine learning tasks.

Identification of Power Quality Disturbances in Electrical Distribution System using Fast Fourier Transforms and Super Learner Ensembles

Currently, the use of non-linear loads and equipment, as well as renewable energy sources injected into the power system, tends to increase. As a result, the waveform of the electrical signal changes, and distortion occurs in the distribution system, which affects the quality and reliability of the electrical system. Importantly, sometimes this leads to malfunctions in protection equipment. This paper presents the algorithm for power quality disturbance (PQD) identification in electrical distribution systems, which involves three main steps: (1) Generating simulated waveforms using a signal processing approach; (2) extracting features using the Fast Fourier Transforms (FFT) technique; and (3) identifying the type of PQD using Super Learner Ensembles (SLE), which employs cross-validation to assess the performance of multiple machine learning models. Subsequently, the model’s efficiency is verified and tested using data from electronic energy meters installed in the distribution system of the Provincial Electricity Authority (PEA). The accuracy resulting from synthetic and experimental data sets is 99.90% and 99.69%, respectively. The results indicate that the model performs well in identifying power quality disturbances and achieves high accuracy.