K-nearest neighbor based on exploratory data analysis of curriculum models of Chinese early childhood education.

This research examines the unique Chinese approaches to implementing the Early Childhood Curriculum (ECC) in Shenzhen and Hong Kong, drawing on School-based Curriculum Development (SBCD) studies. A total of 200 administrators and teachers were interviewed in total, and transcripts from those interviews were examined, cross-checked, and assessed using document analysis and classroom observation. Through interviews that have been conducted by administrators and teachers analyzed by document analysis and classroom observation, the influence of Chinese culture on ECC implementation is explored using the Cultural-Historical Activity Theory (CHAT). An exploratory, inferential, and descriptive statistical approach evaluates the sociocultural mechanism of ECC in Chinese society. The proposed framework utilizes K-Nearest Neighbor (KNN) regression analysis to illustrate how social development leads to cultural fusion and conflicts. The overall sociocultural framework promotes cultural growth and inheritance in China's early childhood education settings.

Prediction of segmental motor outcomes in traumatic spinal cord injury: Advances beyond sum scores.

BACKGROUND AND OBJECTIVES: Neurological and functional recovery after traumatic spinal cord injury (SCI) is highly challenged by the level of the lesion and the high heterogeneity in severity (different degrees of in/complete SCI) and spinal cord syndromes (hemi-, ant-, central-, and posterior cord). So far outcome predictions in clinical trials are limited in targeting sum motor scores of the upper (UEMS) and lower limb (LEMS) while neglecting that the distribution of motor function is essential for functional outcomes. The development of data-driven prediction models of detailed segmental motor recovery for all spinal segments from the level of lesion towards the lowest motor segments will improve the design of rehabilitation programs and the sensitivity of clinical trials. METHODS: This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery of segmental motor scores as the primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for k-nearest neighbor (kNN) matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. The kNN performance was compared to linear and logistic regression models. RESULTS: We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUCwalker = 0.92, AUCself-carer = 0.83) for the kNN algorithm, improving beyond the linear regression task (RMSElinear = 0.98(0.22, 2.57)). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUCwalker = 0.92). We deploy the final historic control model as a web tool for easy user interaction (https://hicsci.ethz.ch/). DISCUSSION: Our approach is the first to provide predictions across all motor segments independent of the level and severity of SCI. We provide a machine learning concept that is highly interpretable, i.e. the prediction formation process is transparent, that has been validated across European and American data sets, and provides reliable and validated algorithms to incorporate external control data to increase sensitivity and feasibility of multinational clinical trials.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Rolling Bearing Fault Diagnosis Based on SABO-VMD and WMH-KNN.

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH-KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH-KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO-VMD and WMH-KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate.

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics.

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor.

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment. Therefore, new reconstruction methods are necessary for underwater SPAD detection to obtain high-resolution images. In this paper, we propose an algorithm that reconstructs high-resolution depth profiles of underwater targets from a time-photon histogram by employing the K-nearest neighbor (KNN) to classify multiple targets and the background. The results contribute to the performance of pixel accumulation and depth estimation algorithms such as pixel cross-correlation and ManiPoP. We use public experimental data sets and underwater simulation data to verify the effectiveness of the proposed algorithm. The results of our algorithm show that the root mean square errors (RMSEs) of land targets and simulated underwater targets are reduced by 57.12% and 23.45%, respectively, achieving high-resolution single-photon depth profile reconstruction.

A hybrid model for the detection of retinal disorders using artificial intelligence techniques.

The prevalence of vision impairment is increasing at an alarming rate. The goal of the study was to create an automated method that uses optical coherence tomography (OCT) to classify retinal disorders into four categories: choroidal neovascularization, diabetic macular edema, drusen, and normal cases. This study proposed a new framework that combines machine learning and deep learning-based techniques. The utilized classifiers were support vector machine (SVM), K-nearest neighbor (K-NN), decision tree (DT), and ensemble model (EM). A feature extractor, the InceptionV3 convolutional neural network, was also employed. The performance of the models was evaluated against nine criteria using a dataset of 18000 OCT images. For the SVM, K-NN, DT, and EM classifiers, the analysis exhibited state-of-the-art performance, with classification accuracies of 99.43%, 99.54%, 97.98%, and 99.31%, respectively. A promising methodology has been introduced for the automatic identification and classification of retinal disorders, leading to reduced human error and saved time.

DTDHM: detection of tandem duplications based on hybrid methods using next-generation sequencing data.

Background: Tandem duplication (TD) is a common and important type of structural variation in the human genome. TDs have been shown to play an essential role in many diseases, including cancer. However, it is difficult to accurately detect TDs due to the uneven distribution of reads and the inherent complexity of next-generation sequencing (NGS) data. Methods: This article proposes a method called DTDHM (detection of tandem duplications based on hybrid methods), which utilizes NGS data to detect TDs in a single sample. DTDHM builds a pipeline that integrates read depth (RD), split read (SR), and paired-end mapping (PEM) signals. To solve the problem of uneven distribution of normal and abnormal samples, DTDHM uses the K-nearest neighbor (KNN) algorithm for multi-feature classification prediction. Then, the qualified split reads and discordant reads are extracted and analyzed to achieve accurate localization of variation sites. This article compares DTDHM with three other methods on 450 simulated datasets and five real datasets. Results: In 450 simulated data samples, DTDHM consistently maintained the highest F1-score. The average F1-score of DTDHM, SVIM, TARDIS, and TIDDIT were 80.0%, 56.2%, 43.4%, and 67.1%, respectively. The F1-score of DTDHM had a small variation range and its detection effect was the most stable and 1.2 times that of the suboptimal method. Most of the boundary biases of DTDHM fluctuated around 20 bp, and its boundary deviation detection ability was better than TARDIS and TIDDIT. In real data experiments, five real sequencing samples (NA19238, NA19239, NA19240, HG00266, and NA12891) were used to test DTDHM. The results showed that DTDHM had the highest overlap density score (ODS) and F1-score of the four methods. Conclusions: Compared with the other three methods, DTDHM achieved excellent results in terms of sensitivity, precision, F1-score, and boundary bias. These results indicate that DTDHM can be used as a reliable tool for detecting TDs from NGS data, especially in the case of low coverage depth and tumor purity samples.

Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects.

In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.

A fault isolation strategy for industrial processes using outlier-degree-based variable contributions.

In industrial process monitoring, it is always a challenging and practical problem to analyze the causes of the system fault by isolating true fault variables from vast amounts of process data. However, the phenomenon of smearing effect occurs by using the traditional contribution analysis-based isolation methods since the defined isolation indices of different variables affect each other. In this paper, a new fault isolation method is proposed based on local outlier factor and improved k-nearest neighbor rule aiming to improve the isolation accuracy. Firstly, the nearest neighbors of each sample are obtained along the direction of a specific variable. Based on the nearest neighbors, the outlier-degree value of the variable is calculated and regarded as the contribution of the variable. Then, the contribution of the variable in all samples are obtained in the same way, among which the maximum one is selected as the isolation threshold value of this variable. During the online monitoring, the contribution of the variable in the newly collected sample is calculated in real time. Once the contribution is greater than the threshold, the variable is judged to be the dominant factor causing the system fault. Two cases on numerical example and Tennessee Eastman process are conducted to evaluate the effectiveness of the proposed method.

Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer.

The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics-achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.

Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction.

Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging.

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle's position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

Authentication of Laying Hen Housing Systems Based on Egg Yolk Using 1H NMR Spectroscopy and Machine Learning.

(1) Background: The authenticity of eggs in relation to the housing system of laying hens is susceptible to food fraud due to the potential for egg mislabeling. (2) Methods: A total of 4188 egg yolks, obtained from four different breeds of laying hens housed in colony cage, barn, free-range, and organic systems, were analyzed using 1H NMR spectroscopy. The data of the resulting 1H NMR spectra were used for different machine learning methods to build classification models for the four housing systems. (3) Results: The comparison of the seven computed models showed that the support vector machine (SVM) model gave the best results with a cross-validation accuracy of 98.5%. The test of classification models with eggs from supermarkets showed that only a maximum of 62.8% of samples were classified according to the housing system labeled on the eggs. (4) Conclusion: The classification models developed in this study included the largest sample size compared to the literature. The SVM model is most suitable for evaluating 1H NMR data in terms of the hen housing system. The test with supermarket samples showed that more authentic samples to analyze influencing factors such as breed, feeding, and housing changes are required.

A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.

BACKGROUND: Recently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM. HYPOTHESIS: To construct and validate prediction models for the risk of VC in patients with T2DM. METHODS: Twenty-three baseline demographic and clinical characteristics were extracted from the electronic medical record system. Ten clinical features were screened with least absolute shrinkage and selection operator method and were used to develop prediction models based on eight machine learning (ML) algorithms (k-nearest neighbor [k-NN], light gradient boosting machine, logistic regression [LR], multilayer perception [(MLP], Naive Bayes [NB], random forest [RF], support vector machine [SVM], XGBoost [XGB]). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and precision. RESULTS: A total of 1407 and 352 patients were retrospectively collected in the training and test sets, respectively. Among the eight models, the AUC value in the NB model was higher than the other models (NB: 0.753, LGB: 0.719, LR: 0.749, MLP: 0.715, RF: 0.722, SVM: 0.689, XGB:0.707, p < .05 for all). The k-NN model achieved the highest sensitivity of 0.75 (95% confidence interval [CI]: 0.633-0.857), the MLP model achieved the highest accuracy of 0.81 (95% CI: 0.767-0.852) and specificity of 0.875 (95% CI: 0.836-0.912). CONCLUSIONS: This study developed a predictive model of VC based on ML and clinical features in type 2 diabetic patients. The NB model is a tool with potential to facilitate clinicians in identifying VC in high-risk patients.

Combining Cardiovascular and Pupil Features Using k-Nearest Neighbor Classifiers to Assess Task Demand, Social Context, and Sentence Accuracy During Listening.

In daily life, both acoustic factors and social context can affect listening effort investment. In laboratory settings, information about listening effort has been deduced from pupil and cardiovascular responses independently. The extent to which these measures can jointly predict listening-related factors is unknown. Here we combined pupil and cardiovascular features to predict acoustic and contextual aspects of speech perception. Data were collected from 29 adults (mean  =  64.6 years, SD  =  9.2) with hearing loss. Participants performed a speech perception task at two individualized signal-to-noise ratios (corresponding to 50% and 80% of sentences correct) and in two social contexts (the presence and absence of two observers). Seven features were extracted per trial: baseline pupil size, peak pupil dilation, mean pupil dilation, interbeat interval, blood volume pulse amplitude, pre-ejection period and pulse arrival time. These features were used to train k-nearest neighbor classifiers to predict task demand, social context and sentence accuracy. The k-fold cross validation on the group-level data revealed above-chance classification accuracies: task demand, 64.4%; social context, 78.3%; and sentence accuracy, 55.1%. However, classification accuracies diminished when the classifiers were trained and tested on data from different participants. Individually trained classifiers (one per participant) performed better than group-level classifiers: 71.7% (SD  =  10.2) for task demand, 88.0% (SD  =  7.5) for social context, and 60.0% (SD  =  13.1) for sentence accuracy. We demonstrated that classifiers trained on group-level physiological data to predict aspects of speech perception generalized poorly to novel participants. Individually calibrated classifiers hold more promise for future applications.

Autism spectrum disorder detection with kNN imputer and machine learning classifiers via questionnaire mode of screening.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. ASD cannot be fully cured, but early-stage diagnosis followed by therapies and rehabilitation helps an autistic person to live a quality life. Clinical diagnosis of ASD symptoms via questionnaire and screening tests such as Autism Spectrum Quotient-10 (AQ-10) and Quantitative Check-list for Autism in Toddlers (Q-chat) are expensive, inaccessible, and time-consuming processes. Machine learning (ML) techniques are beneficial to predict ASD easily at the initial stage of diagnosis. The main aim of this work is to classify ASD and typical developed (TD) class data using ML classifiers. In our work, we have used different ASD data sets of all age groups (toddlers, adults, children, and adolescents) to classify ASD and TD cases. We implemented One-Hot encoding to translate categorical data into numerical data during preprocessing. We then used kNN Imputer with MinMaxScaler feature transformation to handle missing values and data normalization. ASD and TD class data is classified using Support vector machine, k-nearest-neighbor (KNN), random forest (RF), and artificial neural network classifiers. RF gives the best performance in terms of the accuracy of 100% with different training and testing data split for all four types of data sets and has no over-fitting issue. We have also examined our results with already published work, including recent methods like Deep Neural Network (DNN) and Convolution Neural Network (CNN). Even using complex architectures like DNN and CNN, our proposed methods provide the best results with low-complexity models. In contrast, existing methods have shown accuracy upto 98% with log-loss upto 15%. Our proposed methodology demonstrates the improved generalization for real-time ASD detection during clinical trials.

Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features.

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis.

This study introduces an innovative approach for fault diagnosis of a multistage centrifugal pump (MCP) using explanatory ratio (ER) linear discriminant analysis (LDA). Initially, the method addresses the challenge of background noise and interference in vibration signals by identifying a fault-sensitive frequency band (FSFB). From the FSFB, raw hybrid statistical features are extracted in time, frequency, and time-frequency domains, forming a comprehensive feature pool. Recognizing that not all features adequately represent MCP conditions and can reduce classification accuracy, we propose a novel ER-LDA method. ER-LDA evaluates feature importance by calculating the explanatory ratio between interclass distance and intraclass scatteredness, facilitating the selection of discriminative features through LDA. This fusion of ER-based feature assessment and LDA yields the novel ER-LDA technique. The resulting selective feature set is then passed into a k-nearest neighbor (K-NN) algorithm for condition classification, distinguishing between normal, mechanical seal hole, mechanical seal scratch, and impeller defect states of the MCP. The proposed technique surpasses current cutting-edge techniques in fault classification.

Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model.

BACKGROUND: Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes. METHODS: This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability. RESULTS: Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%. CONCLUSIONS: The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.