Multilayer Perceptron Model Research Articles

Prediction of Intraday Electricity Supply Curves

The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. To achieve this, we approximate these curves using a non-uniform grid of points and evaluate the quality of these approximations with a weighted distance, both based on empirical market data. We employ neural network models, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and a Transformer network alongside a naive model for benchmarking. The MLP and CNN models demonstrated significant improvements in predicting these supply curves for the six market sessions.

Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning

Deep neural networks (DNNs) and transfer learning (TL) have been used to improve surface electromyogram (sEMG)-based force estimation. However, prior studies focused mostly on applying TL within one joint, which limits dataset size and diversity. Herein, we investigated cross-joint TL between two upper-limb joints with four DNN architectures using sliding windows. We used two feedforward and two recurrent DNN models with feature engineering and feature learning, respectively. We found that the dependencies between sEMG and force are short-term (<400 ms) and that sliding windows are sufficient to capture them, suggesting that more complicated recurrent structures may not be necessary. Also, using DNN architectures reduced the required sliding window length. A model pre-trained on elbow data was fine-tuned on hand–wrist data, improving force estimation accuracy and reducing the required training data amount. A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. The success of TL between two distinct joints could help enrich the data available for future deep learning-related studies.

Personal and Clinical Predictors of Voice Therapy Outcomes: A Machine Learning Analysis Using the Voice Handicap Index

In this study, we examine the predictive factors influencing the outcomes of voice treatment in patients with voice-related disorders, using the voice handicap index (VHI) as a key assessment tool. By analyzing various personal habits and clinical variables, we identify the primary factors associated with changes when comparing VHI scores before and after voice treatment. For this research, we employed binomial logistic regression, random forest (RF), and a multilayer perceptron (MLP) model to evaluate the effectiveness of voice treatment. The findings reveal that gender (with female patients showing greater improvements in VHI scores compared to male patients), surgical history, voice use status, and voice training status are significant predictors of therapy outcomes. The MLP model demonstrated high accuracy, sensitivity, and specificity, with an area under the curve (AUC) value of 0.87 indicating its potential as a valuable clinical predictive tool; however, the model’s relatively low specificity suggests the need for further refinement to enhance its predictive accuracy. The results of this study provide valuable insights for clinicians and speech–language pathologists in developing personalized treatment strategies to optimize the effectiveness of voice therapy. Future research should prioritize the validation of these findings in larger and more diverse population samples. Furthermore, it is essential to explore additional predictive variables in order to enhance the model’s accuracy across different types of voice disorders.

Medical Image, Analysis and Visualization using Image Processing

This project presents an automated approach for detecting brain tumor using Magnetic Resonance Imaging (MRI) and image processing techniques. Traditional methods rely on human inspection, which can be error-prone and time consuming. Our framework consists of several modules: acquiring MRI images, preprocessing to enhance image quality (including noise reduction and edge detection), and segmenting the tumor using the Watershed algorithm. We extract texture features from the segmented images using the Gray Level Co-occurrence Matrix (GLCM), focusing on metrics like energy, contrast, correlation, and homogeneity. Finally, we classify the MRI images as normal or abnormal using a Multilayer Perceptron (MLP) model. This automated detection process improves the efficiency of tumor identification, providing insights into the size, shape, and position of the tumor while alleviating the workload of radiologists.

A novel framework for debris flow susceptibility assessment considering the uncertainty of sample selection

The uncertainty arising from random sampling of non-debris flow samples significantly impacts the accuracy of debris flow susceptibility assessments (DFSA). This study introduces a novel uncertainty elimination method, Kernel Density Estimation (KDE), and compares it with Mean and Maximum Probability Analysis (MPA) methods. Furthermore, we investigate the responses of four commonly used machine learning models to sampling uncertainty, comparing two structurally similar models (Random Forest (RF) and Extremely Randomized Trees (ERT)) with two structurally different models (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)). The results indicate that the application of these uncertainty elimination methods can significantly enhance AUC values and zoning accuracy, with the KDE method outperforming the others. Specifically, the AUC values based on KDE for RF, ERT, SVM, and MLP are 0.995, 0.999, 0.999, and 0.853, respectively. The corresponding zoning accuracy for these models is 1.00, 1.00, 1.00, and 0.78, respectively. The study further reveals that the responses to sampling uncertainty vary by model architecture: RF, ERT, and SVM typically exhibit bimodal normal distributions, while the MLP model shows a unimodal distribution. Additionally, MLP is more sensitive to variations in negative samples, whereas RF and ERT are less affected due to the ensemble structure.

Advancing mango leaf variant identification with a robust multi-layer perceptron model

Mango, often regarded as the “king of fruits,” holds a significant position in Bangladesh’s agricultural landscape due to its popularity among the general population. However, identifying different types of mangoes, especially from mango leaves, poses a challenge for most people. While some studies have focused on mango type identification using fruit images, limited work has been done on classifying mango types based on leaf images. Early identification of mango types through leaf analysis is crucial for taking proactive steps in the cultivation process. This research introduces a novel multi-layer perceptron model called WaveVisionNet, designed to address this challenge using mango leaf datasets collected from five regions in Bangladesh. The MangoFolioBD dataset, comprising 16,646 annotated high-resolution images of mango leaves, has been curated and augmented to enhance robustness in real-world conditions. To validate the model, WaveVisionNet is evaluated on both the publicly available dataset and the MangoFolioBD dataset, achieving accuracy rates of 96.11% and 95.21%, respectively, outperforming state-of-the-art models such as Vision Transformer and transfer learning models. The model effectively combines the strengths of lightweight Convolutional Neural Networks and noise-resistant techniques, allowing for accurate analysis of mango leaf images while minimizing the impact of noise and environmental factors. The application of the WaveVisionNet model for automated mango leaf identification offers significant benefits to farmers, agricultural experts, agri-tech companies, government agencies, and consumers by enabling precise diagnosis of plant health, enhancing agricultural practices, and ultimately improving crop yields and quality.

Self-learning salp swarm algorithm for global optimization and its application in multi-layer perceptron model training

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents’ abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA.

Correction of Sensible Heat Flux from Flux-Gradient Method to Eddy Covariance Method Based on Multi-Layer Perceptron

Abstract. Understanding surface-atmosphere interactions is critical for environmental and meteorological studies. Sensible heat flux, a key component in this interaction, is typically measured using methods such as Eddy Covariance (EC) and Flux-Gradient (FG). The EC method, known for its high temporal resolution and direct measurement capabilities of wind speed, temperature, and humidity changes, requires the use of expensive and complex equipment, making it costly and challenging to implement. On the contrary, the FG method is more accessible and economical, relying on simpler instruments, but often lacks the precision of the EC method. To harness the benefits of both methods, this article uses the Multi-Layer Perceptron (MLP) machine learning method to enhance the accuracy of the FG method's sensible heat flux calculations. Through the MLP model, this paper aims to determine the optimal parameter settings for the specific measurement environment, thereby improving the FG methods accuracy. The data was measured from Guandu Village, Anhui Province, China. This research seeks to demonstrate that the trained MLP model can be applied to similar measurement environments, thus enhancing the FG method's applicability and precision.

How Resilient Are Kolmogorov–Arnold Networks in Classification Tasks? A Robustness Investigation

Kolmogorov–Arnold Networks (KANs) are a novel class of neural network architectures based on the Kolmogorov–Arnold representation theorem, which has demonstrated potential advantages in accuracy and interpretability over Multilayer Perceptron (MLP) models. This paper comprehensively evaluates the robustness of various KAN architectures—including KAN, KAN-Mixer, KANConv_KAN, and KANConv_MLP—against adversarial attacks, which constitute a critical aspect that has been underexplored in current research. We compare these models with MLP-based architectures such as MLP, MLP-Mixer, and ConvNet_MLP across three traffic sign classification datasets: GTSRB, BTSD, and CTSD. The models were subjected to various adversarial attacks (FGSM, PGD, CW, and BIM) with varying perturbation levels and were trained under different strategies, including standard training, adversarial training, and Randomized Smoothing. Our experimental results demonstrate that KAN-based models, particularly the KAN-Mixer, exhibit superior robustness to adversarial attacks compared to their MLP counterparts. Specifically, the KAN-Mixer consistently achieved lower Success Attack Rates (SARs) and Degrees of Change (DoCs) across most attack types and datasets while maintaining high accuracy on clean data. For instance, under FGSM attacks with ϵ=0.01, the KAN-Mixer outperformed the MLP-Mixer by maintaining higher accuracy and lower SARs. Adversarial training and Randomized Smoothing further enhanced the robustness of KAN-based models, with t-SNE visualizations revealing more stable latent space representations under adversarial perturbations. These findings underscore the potential of KAN architectures to improve neural network security and reliability in adversarial settings.

Using DL Models in the Service Layer to Enhance the Fault Tolerance of IoT Networks

In an IoT network, the networked servers form a service layer, providing services to the users and the devices. The request to the service servers is routed through the gateway on one side of the services layer and the networked controllers on the other side. Data are transported from the sensors/devices through cluster heads en route to base stations and the controllers to the service servers, where the data are processed and sent for storage in the cloud through gateways. When any device is broken down or becomes non-operational, the inputs are not sensed, creating a gap in the data. The data transmitted from the devices would then become an incomplete flow; such data are not suitable for undertaking data analytics or predictions. The missing data must be first identified as the data flow and estimated or predicated to complete the data before they are transmitted through the cloud for storage and subsequent retrievals. This paper proposes a recurrent (RNN) neural network to predict the missing data. Two models are tested to predict the missing data: the multi-layer perceptron (MLP) model and a long short-term memory (LSTM)-based RNN model. The RNN-based model provides 99.66% accurate data prediction compared to other models.

MODEL OF MAXIMAL WEIGHTS INVERSE CHAINS FOR THE ANALYSIS OF THE INFLUENCE FACTORS OF THE SOFTWARE COMPLEXES SUPPORT

Context. The problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of multi-layer perceptron models into the models of subjective perception of the object of software complexes support, as well as the applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode, is considered. The object of research is the influencing factors of support of software complexes. Objective – the goal of the work is to develop a model of inverse chains of maximum weights for the analysis of influencing factors of the software complexes support. Method. A model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support was developed for the analysis of the influencing factors of the software complexes support. The developed model provides possibility to identify and form feedback chains of maximum weights for the identification and further analysis of influencing factors that are reflected into the results of the object perception (the supported software complex or its support processes), by the relevant subjects of interaction which directly or indirectly interact with it. Results. Results of the resolved applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode have been provided as an example of the applied practical use of the developed model. The output results of the developed models functioning – are the reverse chains of maximum weights. In the future, the results obtained by the developed model are used to solve the applied-scientific problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of the appropriate models of multilayer perceptron inside the models of subjective perception of the software complexes support. So the developed model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support resolves this applied-scientific problem, initially caused by the implementation of the corresponding multilayer perceptron models inside the model of the subjective perception of the object of software complexessupport. The developed model provides the possibility of carrying out a qualitative analysis of the transformation of the input characteristics of the object of support into the output resulting characteristics of its subjective perception. Conclusions. Developed model allows to resolve the described problems. At the same time, the developed model improves the classical understanding of multilayer perceptron artificial neural networks, as it introduces an additional value to the neurons of hidden layers, which (starting from now) are able to perform a completely new role of influencing factors markers, while in the classical understanding of multilayer perceptron artificial neural networks they did not perform any functions other than arithmetic to ensure the possibility of correct learning and functioning of a multilayer perceptron artificial neural networks.

Sediment Simulation Using Multi Layer Perceptron (MLP), Co-Active Neuro-Fuzzy Inference System (CANFIS) and Multiple Linear Regression Tecniques (MLR) for Hurdag Watershed

Sediment modeling plays a crucial role in sustainable water resources planning, development, and management. Techniques like the Multilayer Perceptron (MLP), Co-active Neuro-Fuzzy Inference System (CANFIS), and Multiple Linear Regression (MLR) have proven effective for sediment modeling and forecasting. This study aimed to develop and assess the applicability of MLP, CANFIS, and MLR models by training and testing them during the monsoon season (June to September) for the Hurdag watershed in the Damodar-Barakar basin, located in Hazaribagh district, Jharkhand, India. Daily rainfall, runoff (or streamflow), and sediment concentration data from 1997 to 2006 were used, with the data split into two sets: training set (1997–2004) and a testing set (2005–2006). The analysis was conducted using NeuroSolution 5.0 software and Microsoft Excel for performance evaluation indices. The best input combinations for sediment yield simulation were identified, and 10 optimal models were selected from 31 different input combinations. These input combinations were applied to the network for training using the back propagation algorithm for MLP and Gaussian and generalized bell membership functions for CANFIS models. Multiple networks were trained individually, with the most accurate predictions during testing being chosen as the best models. The models’ performance was evaluated using statistical indices such as root mean squared error (RMSE), coefficient of efficiency (CE), and correlation coefficient (r). The results showed that MLP and CANFIS models performed best in predicting sediment concentration for the Hurdag watershed, whereas MLR models showed poor performance for the given dataset. Specifically, sediment concentration for the current day could be modeled using current day rainfall and runoff data (MLP-7), while runoff could be simulated using the previous day's rainfall data (MLP-2).

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

Phosphate-solubilizing fungus (PSF) - mediated phosphorous solubilization and validation through Artificial intelligence computation.

Phosphate-solubilizing fungus (PSF) strain alaromyces funiculosus was investigated for phosphorus solubilization, utilizing a range of pH levels and phosphate sources, followed by data confirmation through artificial intelligence modeling. T. funiculosus strain was exposed to five different phosphate sources [Ca3(PO4)2, FePO4, CaHPO4, AlPO4, and phytin] at different pH levels (4.5, 5.5, 6.5, 7.0, and 7.5). ANOVA, Pareto charts, and normal plots were used for analyzing the data. Artificial intelligence-based multilayer perceptron (MLP), random forest (RF) and extreme gradient boosting (XGBoost) models were used for data validation and prediction. Five-fold more phosphate (P) solubility by T. funiculosus was registered as compared to the control. The maximum soluble P was found at pH 4.5 (318324 ppb) and CaHPO4 (444045 ppb). Combination of phytin × 4.5 pH yielded the highest dissolved phosphorus (1537988 ppb), followed by 127458 ppb from the control × 4.5 pH. Pareto chart and normal plot analysis showedthe negative impact of pH (B), pH × F/C (fungus/control) × P-Source (ABC), and F/C (A) factor. Whereas pH × P-Source (AC) and P-Source (C) has positive impact on P solubility. The maximum R2 scores showed the order of RF (0.944) > MLP (0.938) > XGBoost (0.899). T. funiculosus strain has a grain potential for sustainable use for different types of phosphate sources. Application AI/ML models based on different performance metrics predicted the validated the attained results. In future research, it is recommended to check the efficacy of developed strategy under field conditions and to check the impact on soil and plant.

Daily Runoff Forecasting Using Novel Optimized Machine Learning Methods

Accurate runoff forecasting is crucial for effective water resource management, yet existing models often face challenges due to the complexity of hydrological systems. This study addresses these challenges by introducing a novel bio-inspired metaheuristic algorithm, Artificial Rabbits Optimization (ARO), integrated with various machine learning (ML) models for runoff forecasting in the Carson and Chehalis River basins. The ARO-ML models are rigorously evaluated, demonstrating their superior ability to enhance forecasting accuracy compared to widely used ML techniques, including gradient boosting (GB), multi-layer perceptron (MLP), and K-nearest neighbor regression (KNN). In the Carson River, the GB model achieves the highest forecasting accuracy, which is significantly improved by ARO, resulting in a 24.8% reduction in root mean square error (RMSE). The MLP model also benefits notably from ARO, with RMSE improvements of 4.8% and a substantial 48.9% reduction in mean absolute error (MAE). The ARO-optimized KNN model shows exceptional performance, surpassing the baseline KNN during testing. In the Chehalis River Basin, ARO integration leads to substantial improvements in both GB and MLP models across various performance metrics during training and testing. Crucially, the results indicate that in both river basins, rainfall in preceding days has the most significant influence on streamflow forecasts, followed by air temperature. This study advances hydrological forecasting by validating the efficacy of ARO-ML models, offering a robust framework for enhanced predictive accuracy and providing deeper insights into the key drivers of streamflow in diverse hydrological contexts.

Study on characteristics and prediction of the pressure drag of the swept strut under supersonic wide-range conditions

In this paper, the numerical simulation of the swept strut ramjet combustor was carried out under wide-range conditions (Ma3 = 1.8 ∼ 5.0), and the pressure drag characteristics of the swept strut were discussed. The results show that the pressure drag characteristics of the swept strut are related to the Mach number of the combustor inlet and the swept angle of the strut. The decreased boundary of the strut pressure drag coefficient gradually advances with the decrease of the Mach number of the combustor inlet. When Ma3 = 1.8 ∼ 2.0, the pressure drag reduction boundary is α = 15°. When Ma3 = 2.2 ∼ 2.8, the pressure drag reduction boundary is α = 45°. When Ma3 = 3.0 ∼ 4.0, the pressure drag reduction boundary is α = 60°. When Ma3 = 5.0, the pressure drag reduction boundary is α = 65°. In addition, with the decrease of the Mach number of the combustor inlet, the pressure drag reduction performance benefit brought by increasing the swept angle of the strut will gradually increase. Furthermore, a pressure drag coefficient prediction model suitable for wide-range conditions and multiple configurations of swept struts was proposed based on deep learning. The prediction model consists of two parts in series, which includes the prediction model of the surface pressure coefficient of the swept strut based on multilayer perceptron (MLP) and the prediction model of the pressure drag coefficient of the swept strut based on convolutional neural network (CNN). To improve the prediction accuracy of the MLP model, new training samples were added based on the ensemble-based uncertainty quantification, and the improved MLP model was obtained by retraining. The results show that both the two prediction models have high prediction accuracy under the effect of multiple complex flow characteristics on the strut. The results of this study are helpful to provide a reference for the aerodynamic drag reduction design of the strut in the wide-speed supersonic combustor.

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey—Rape honey

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey—rape honey was determined using fluorescence spectroscopy combined with multilayer perceptron (MLP) deep learning, without the need for any prior feature extraction or pre-processing. A total of 91 raw fluorescence intensity data of the real and adulterated honey samples at a fixed excitation wavelength of 280 nm were first matrixed, and all data were then categorized into a training set, a validation set, and a test set with numbers of 64, 16, and 11, respectively. The connection with dropout was selected to build and link the MLP internal network. The activation function, learning rate, optimizer, and number of epochs were among the hyperparameters of the MLP neural network that were tuned. A good MLP deep learning network model for determining the authenticity of monofloral honey, rape honey, was developed after constant validation and debugging. According to the accuracy curve of the MLP model, the accuracy of the training set increased with the number of epochs and eventually converged to 100 %, while the accuracy of the validation set could be well stabilized at about 100 % after 5000 epochs. Finally, the accuracy of the MLP model on the test set was close to 100 %. According to our findings, the MLP neural network and fluorescence intensity have great potential applications in identifying the authenticity of honey.

Compressive strength prediction of high-performance concrete using MLP model

ABSTRACT The remarkable mechanical properties of high-performance concrete (HPC) make it indispensable in a wide range of engineering applications. Predicting HPC’s compressive strength with precision is essential to guaranteeing its structural stability and longevity. The present work introduces a novel method for predicting HPC compressive strength (CS) by utilizing the Multi-layer Perceptron (MLP) model in conjunction with three distinct optimizers, namely Northern Goshawk Optimization (NGO), Manta Ray Foraging Optimization (MRFO), and Atom Search Optimization (ASO). An effective method for capturing complex relationships between input and output variables is the multi-layer perceptron (MLP) in artificial neural networks. Developing a dependable, robust predictive model is the goal of training the MLP model on a variety of datasets of HPC mixtures and CS. As evidenced by the MLNG model’s R2 value of 0.994 and RMSE of 1.3572, the joint efforts of MRFO, NGO, and ASO during the optimization phase improve the MLP model and produce promising results. These results clarify how different optimizers play a crucial role in improving the accuracy of HPC compressive strength predictions made with the MLP model. This information helps make better decisions regarding design and construction methods for HPC.

Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.

Acute kidney injury (AKI) and contrast-induced nephropathy (CIN) are common complications following percutaneous coronary intervention (PCI) or coronary angiography (CAG), presenting significant clinical challenges. Machine learning (ML) models offer promise for improving patient outcomes through early detection and intervention strategies. A comprehensive literature search following PRISMA guidelines was conducted in PubMed, Scopus, and Embase from inception to June 11, 2024. Study characteristics, ML models, performance metrics (AUC, accuracy, sensitivity, specificity, precision), and risk-of-bias assessment using the PROBAST tool were extracted. Statistical analysis used a random-effects model to pool AUC values, with heterogeneity assessed via the I2 statistic. From 431 initial studies, 14 met the inclusion criteria. Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) models showed the highest pooled AUCs of 0.87 (95% CI: 0.82-0.92) and 0.85 (95% CI: 0.80-0.90), respectively, with low heterogeneity (I2 < 30%). Random Forest (RF) had a similar AUC of 0.85 (95% CI: 0.78-0.92) but significant heterogeneity (I2 > 90%). Multilayer perceptron (MLP) and XGBoost models had moderate pooled AUCs of 0.79 (95% CI: 0.74-0.84) with high heterogeneity. RF showed strong accuracy (0.83, 95% CI: 0.70-0.96), while SVM had balanced sensitivity (0.69, 95% CI: 0.63-0.75) and specificity (0.73, 95% CI: 0.60-0.86). Age, serum creatinine, left ventricular ejection fraction, and hemoglobin consistently influenced model efficacy. GBM and SVM models, with robust AUCs and low heterogeneity, are effective in predicting AKI and CIN post-PCI/CAG. RF, MLP, and XGBoost, despite competitive AUCs, showed considerable heterogeneity, emphasizing the need for further validation.

Detecting the impact of diagnostic procedures in Pap-positive women on anxiety using artificial neural networks.

Women who receive a result of an abnormal Papanicolaou (Pap) smear can fail to participate in follow up procedures, and this is often due to anxiety. This study aimed to apply artificial neural networks (ANN) in prediction of anxiety in women with an abnormal Pap smear test, prior to and following diagnostic procedures. One hundred-seventy two women who received an abnormal Pap screening result took part in this study, completing a questionnaire about socio-demographic characteristics and Hospital Anxiety and Depression Scale (HADS), right before and two to four weeks after diagnostics (i.e. colposcopy/biopsy/endocervical curettage). A feedforward back-propagation multilayer perceptron model was applied in analysis. Prior to diagnostic procedures 50.0% of women experienced anxiety, while after diagnostics anxiety was present in 61.6% of women. The correlation-based feature selection showed that anxiety prior to diagnostic procedures was associated with the use of sedatives, worry score, depression score, and score for concern about health consequences. For anxiety following diagnostics, predictors included rural place of residence, depression score, history of spontaneous abortion, and score for tension and discomfort during colposcopy. The ANN models yielded highly accurate anxiety prediction both prior and after diagnostics, 76.47% and 85.30%, respectively. The presented findings can aid in identification of those women with a positive Pap screening test who could develop anxiety and thus represent the target group for psychological support, which would consequently improve adherence to follow-up diagnostics and enable timely treatment, finally reducing complications and fatal outcome.