Good Classification Performance Research Articles

Fatigue trajectories by wearable remote monitoring of breast cancer patients during radiotherapy

The aim of this pilot study was to assess the compliance of breast cancer (BC) patients with fitness tracker (FT) monitoring program during radiotherapy (RT) and to characterize radiation-induced fatigue (RIF) status through objective evaluation using FT-collected parameters. Thirty-six BC patients were invited to wear FT during their RT course for continuous monitoring of heart rate (HR) and step counts (STP). RIF assessment was performed weekly, according to CTCAE v5.0 and dichotomized into G0 vs. any-grade. A novel concept based on patient Repeated Activity Window (RAW) was introduced to evaluate HR and STP variations during RT. Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. RIF of any grade was reported by 17 out of 36 patients (47%) included in the study. None of patient clinical variables were significantly correlated with RIF. All patients accepted the FT monitoring program, and for 32 patients FT collection efficiency was greater than 60%. For each patient, a distinct distribution of RAWs was identifiable over RT and across the entire patient cohort, with a total of 7950 RAWs processed. Six features related to RAWs, HR and STP were identified as associated with RIF. The best-performing classifier was the Bagged Trees model, showing a cross-validated ROC-AUC of 89% (95% CI 88–90%). This study confirms the feasibility of continuous biomedical monitoring of BC patients by FT. We successfully identify objective indicators of RIF through HR and STP variation measures within each patient’s RAW, thus providing a novel and practical approach to assess and manage RIF. This can significantly aid medical staff in evaluating RIF trajectories, potentially leading to better individualized care strategies and improved patient outcomes.

Erythrocytic α-Synuclein in Parkinson’s Disease and Progressive Supranuclear Palsy—A Pilot Study

Background/Objectives: The current research examines the accuracy of α-synuclein in RBCs as a diagnostic biomarker for PD and PSP, despite their distinct molecular etiologies. Methods: We used ELISA to measure total, oligomeric, and p129-α-synuclein levels in erythrocytes from 8 PSP patients, 19 PD patients, and 18 healthy controls (HCs). The classification performances of RBC α-synuclein levels were investigated by receiver operator characteristic (ROC) curve. We also evaluated a possible correlation between RBC α-synuclein level and the biological and clinical features of our cohorts. Results: RBC total α-synuclein was higher in PSP patients compared to both PD patients and HCs, achieving good classification performance (AUC: 0.853) in distinguishing PSP patients from PD patients, with a sensitivity of 100% and a specificity of 70.6%; moreover, the levels of this biomarker positively correlated with disease severity in PSP group. Regarding oligomeric α-synuclein and p129-α-synuclein, the latter was slightly increased in RBCs from PSP patients compared to HCs, but no correlations were detected. Conclusions: Although these findings need to be confirmed in larger studies, our pilot work suggests that RBC total α-synuclein may represent a potential molecular biomarker for the differential diagnosis and clinical staging of PSP.

Classification of Multi-Parametric Body MRI Series Using Deep Learning.

Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep learning-based classifiers of ResNet, EfficientNet, and DenseNet are trained to classify 8 different MRI series, and their performance is compared. Then, the best-performing classifier is identified, and its classification capability under the setting of different training data quantities is studied. Also, the model is evaluated on the out-of-training-distribution datasets. Moreover, the model is trained using mpMRI exams obtained from different scanners in two training strategies, and its performance is tested. Experimental results show that the DenseNet-121 model achieves the highest F1-score and accuracy of 0.966 and 0.972 over the other classification models with p-value 0.05. The model shows greater than 0.95 accuracy when trained with over 729 studies of the training data, whose performance improves as the training data quantities grow larger. On the external data with the DLDS and CPTAC-UCEC datasets, the model yields 0.872 and 0.810 accuracy for each. These results indicate that in both the internal and external datasets, the DenseNet-121 model attains high accuracy for the task of classifying 8 body MRI series types.

Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve

Background and objective:Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study, we propose a hybrid pipeline that, starting from plasma glucose data acquired during the Oral Glucose Tolerance Test (OGTT), allows us to classify the two types of patients through computational modeling and artificial intelligence. Methods:The proposed hybrid pipeline integrates a classical mechanistic model of delayed differential equations (DDE) that describes glucose–insulin dynamics with machine learning (ML) methods. Ad hoc techniques, including structural identifiability analysis, have been employed for refining and evaluating the mathematical model. Additionally, a dedicated pipeline for identifying and optimizing model parameters has been applied to obtain reliable estimates. Robust feature extraction and classifier selection processes were developed to ensure the optimal choice of the best-performing classifier. Results:By leveraging parameters estimated from the mechanistic model alongside easily obtainable patient information (such as glucose levels at 30 and 120 min post-OGTT, glycated hemoglobin (Hb1Ac), body mass index (BMI), and waist circumference), our approach facilitates accurate classification of patients, enabling tailored therapeutic interventions. Conclusion:Initial findings, focusing on correctly categorizing patients with BED based on metabolic data, demonstrate promising outcomes. These results suggest significant potential for refinement, including exploration of alternative mechanistic models and machine learning algorithms, to enhance classification accuracy and therapeutic strategies.

Effective Machine Learning Techniques for Dealing with Poor Credit Data

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.

From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer.

Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

Artificial intelligence-enhanced electrocardiography predicts 10-year risk of atherosclerotic cardiovascular disease

Abstract Background The ACC/AHA pooled cohort equations (PCE) calculates the 10-year primary risk of atherosclerotic cardiovascular disease (ASCVD), an indication to initiate primary prevention statin therapy in international guidelines. The validity of PCE is limited by its overestimation of risk in high-risk cohorts and an ethnic heterogeneity. Electrocardiography (ECG) has not been incorporated in current ASCVD risk estimation tools. Artificial Intelligence (AI)-based models are capable to capture patterns that are informative for cardiovascular prognosis. Purpose We explored the predictive value of a discrete-time survival model in 10-year ASCVD risk in comparison to PCE. Methods We developed an AI-ECG model to predict the 10-year ASCVD risk in an unselected secondary care population from the USA comprised of 1,163,401 ECGs from 189,539 patients. Data was split into training/validation and hold-out test sets by patient ID. Our AI-ECG model uses deep learning with a residual convolutional neural network with a discrete-time survival loss function to output subject-specific survival curves, accounting for both time to event and censorship (i.e., loss to follow up). We compared the performance of out AI-ECG model using a subset of outpatient data (n=4590) of the BIDMC dataset to PCE and ASCVD risk factors including systolic blood pressure, total cholesterol, HDL cholesterol, hypertension, smoking history, diabetes mellitus, and ethnicity. Performances were evaluated using cox survival analysis. Our AI-ECG model was externally validated in the UKB Biobank (UKB), where we compared the performance of our AI-ECG model to predictions of existing AI-ECG models such as the Stanford Estimator of Electrocardiogram Risk (SEER) model. Results Our AI-ECG successfully predicted future ASCVD events in individuals (C-index 0.721 (0.719-0.723), 5-year AUC 0.761 (0.758-0.763), and differentiated between high and low risk group with an adjusted HR of 2.33 (2.26-2.41). Upon internal validation our AI-ECG model showed a superior performance (C-index 0.679 (0.651-0.708)) than PCE (0.605 (0.577-0.634)) and all risk factors combined (C index 0.642 (0.613-0.672)). When combining AI-ECG predictions with all risk factors, the model showed additional predictive value (0.686 (0.657-0.715)) (Figure 1). Our AI-ECG model showed modest predictive value within the UKB dataset (C-index 0.655 (0.637-0.673)), but significantly outperformed the SEER model (C-index 0.547 (0.527-0.567), p <0.0001 for comparison with AIRE). Conclusion We have shown the potential of AI-ECG models in predicting future ASCVD risks. AI-ECG models demonstrated accurate discrete time survival prediction and good classification performance superior to that of the SEER model, as well as PCE and ASCVD risk factors. . Accurate risk assessment of ASCVD may help prevent adverse events in high risk subjects and save unnecessary pharmacological interventios in low risk subjects.Figure 1

Development and validation of machine learning models for young-onset colorectal cancer risk stratification

Incidence of young-onset colorectal cancer (YOCRC, younger than 50) has significantly increased worldwide. The performance of fecal immunochemical test in detecting YOCRC is unsatisfactory. Using routine clinical data, we aimed to develop machine learning (ML) models to identify individuals with high-risk YOCRC who require further colonoscopy. We retrospectively extracted data of 10,874 young individuals. Multiple supervised ML techniques were devised to distinguish individuals with and without CRC, classifiers were trained, internally validated and temporally validated. In internal validation cohort, Random Forest (RF) ML model demonstrated good performance with AUC of 0.859 and highest recall of 0.840. In temporal validation cohort, the RF ML model also exhibited good classification performance, achieving AUC of 0.888 and highest recall of 0.872. RF algorithm-based approach is effective and feasible in YOCRC risk stratification. This could be valuable in assessing the risk of YOCRC so that clinical management, including further colonoscopy, can be subsequently made. (Registration: This study was registered with ClinicalTrials.gov (NCT06342622) on March 15, 2024.).

Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data

ObjectiveThis study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. MethodsTwenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. ResultsAmong 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. ConclusionsThe application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA.

Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study.

The COVID-19 pandemic has exposed a number of key challenges that need to be urgently addressed. Mass spectrometric studies of blood plasma proteomics provide a deep understanding of the relationship between the severe course of infection and activation of specific pathophysiological pathways. Analysis of plasma proteins in whole blood may also be relevant for the pandemic as it requires minimal sample preparation. The frozen whole blood samples were used to analyze 203 plasma proteins using multiple reaction monitoring (MRM) mass spectrometry and stable isotope-labeled peptide standards (SIS). A total of 131 samples (FRCC, Russia) from patients with mild (n=41), moderate (n=39) and severe (n=19) COVID-19 infection and healthy controls (n=32) were analyzed. Levels of 94 proteins were quantified and compared. Significant differences between all of the groups were revealed for 44 proteins. Changes in the levels of 61 reproducible COVID-19 markers (SERPINA3, SERPING1, ORM1, HRG, LBP, APOA1, AHSG, AFM, ITIH2, etc.) were consistent with studies performed with serum/plasma samples. The best-performing classifier built with 10 proteins achieved the best combination of ROC-AUC (0.97-0.98) and accuracy (0.90-0.93) metrics and distinguished patients from controls, as well as patients by severity. Here, for the first time, frozen whole blood samples were used for proteomic analysis and assessment of the status of patients with COVID-19. The results obtained with frozen whole blood samples are consistent with those from plasma and serum.

Identifying power relationships in conversations: the case of Pygmalion

Abstract Power relationships express one party’s dominance, control, influence, and authority over the other. In this article, and using state-of-the-art AI tools, we show that power relationships can be automatically identified in textual data. Generating thousands of synthetic utterances expressing either dominance or compliance, we trained/ran three models that showed good classification performance. Moreover, using GPT-4, we present a novel method for presenting power asymmetry in conversations and visualizing the dynamics of power relationships over time. This methodology is presented and illustrated by analyzing a case study—The play Pygmalion by George Bernard Show.

Identifying Key Factors for Securing a Champions League Position in French Ligue 1 Using Explainable Machine Learning Techniques

Introduction: Performance analysis is essential for coaches and a topic of extensive research. The advancement of technology and Artificial Intelligence (AI) techniques has revolutionized sports analytics. Aim: The primary aim of this article is to present a robust, explainable machine learning (ML) model that identifies the key factors that contribute to securing one of the top three positions in the standings of the French Ligue 1, ensuring participation in the UEFA Champions League for the following season. Materials and Methods: This retrospective observational study analyzed data from all 380 matches of the 2022–23 French Ligue 1 season. The data were obtained from the publicly-accessed website “whoscored” and included 34 performance indicators. This study employed Sequential Forward Feature Selection (SFFS) and various ML algorithms, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), to create a robust, explainable model. The SHAP (SHapley Additive Explanations) model was used to enhance model interpretability. Results: The K-means Cluster Analysis categorized teams into groups (TOP TEAMS, 3 teams/REST TEAMS, 17 teams), and the ML models provided significant insights into the factors influencing league standings. The LR classifier was the best-performing classifier, achieving an accuracy of 75.13%, a recall of 76.32%, an F1-score of 48.03%, and a precision of 35.17%. “SHORT PASSES” and “THROUGH BALLS” were features found to positively influence the model’s predictions, while “TACKLES ATTEMPTED” and “LONG BALLS” had a negative impact. Conclusions: Our model provided satisfactory predictive accuracy and clear interpretability of results, which gave useful information to stakeholders. Specifically, our model suggests adopting a strategy during the ball possession phase that relies on short passes (avoiding long ones) and aiming to enter the attacking third and the opponent’s penalty area with through balls.

Staining-Independent Malaria Parasite Detection and Life Stage Classification in Blood Smear Images

Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance. The future directions include collecting more data and exploring more sophisticated algorithms.

Radiomics-based machine learning models for differentiating pathological subtypes in cervical cancer: a multicenter study.

This study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC). Pretreatment 18F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers. Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different model was assessed and compared using the DeLong test. A total of 227 patients with locally advanced cervical cancer were enrolled in this study (N=136 for the training cohort, N=59 for the internal validation cohort, and N=32 for the external validation cohort). The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% confidence interval [CI], 0.715-0.986) in the internal validation cohort, which were higher than those of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339-0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in either the training cohort (z=0.940, P=0.347) or the internal validation cohort (z=0.285, P=0.776). In the external validation cohort, the lightGBM-based PET radiomics model achieved good discrimination between SCC and AC (AUC = 0.730). The lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.

Double-kernel based Bayesian approximation broad learning system with dropout

Broad learning system (BLS) is an efficient incremental learning machine algorithm. However, there are some disadvantages in such an algorithm. For example, the number of hidden layer nodes needs to be manually adjusted during the training process, meanwhile the large uncertainty will be caused by two random mappings. To solve these problems, based on the optimization ability of the kernel function, a double-kernel broad learning system (DKBLS) is proposed to eliminate the uncertainty of random mapping by using additive kernel strategy. Meanwhile, to reduce the computing costs and training time of DKBLS, a double-kernel based bayesian approximation broad learning system with dropout (Dropout-DKBLS) is further proposed. Ablation experiments show that the output accuracy of Dropout-DKBLS does not decrease even if the node is dropped. In addition, function approximation experiments show that DKBLS and Dropout-DKBLS have good robustness and can accurately predict noise data. The regression and classification experiments on multiple datasets are compared with the latest kernel-based learning methods. The comparison results show that both DKBLS and Dropout-DKBLS have good regression and classification performance. By further comparing the training time of these kernel-based learning methods, we prove that the Dropout-DKBLS can reduce the computational cost while ensuring the output accuracy.

Sensor Fault Detection and Classification Using Multi-Step-Ahead Prediction with an Long Short-Term Memoery (LSTM) Autoencoder

The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (LSTM-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method’s functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance.

The Influence of Work-Life Balance and Workload on Employee Performance at Bank BTN Tasikmalaya Branch Office

This research purposse to determine and analyze the picture of work-life balance, workload and employee performance at Bank BTN Tasikmalaya Branch Office. The influence of work-life balance and workload on employee performance simultaneously at Bank BTN Tasikmalaya Branch Office. The influence of work-life balance on employee performance partially at Bank BTN Tasikmalaya Branch Office and the influence of workload on employee performance partially at Bank BTN Tasikmalaya Branch Office. The method used in this research is a survey with a descriptive approach. With a research sample of 35 collection department employees as respondents. The data used is primary and secondary data. The analytical tool used in this research is multiple linear regression using the SPSS version 26 support application. The test results show that work-life balance has a good classification, workload has a good classification and employee performance has a high classification, simultaneously showing the variables used. in the research, work-life balance and workload have a significant effect on employee performance at Bank BTN Tasikmalaya Branch Office. Partially, there is a significant influence of workload on employee performance, while work-life balance does not have a significant influence on employee performance.

Radiomics based on diffusion tensor imaging and 3D T1-weighted MRI for essential tremor diagnosis.

Due to the absence of biomarkers, the misdiagnosis of essential tremor (ET) with other tremor diseases and enhanced physiologic tremor is very common in practice. Combined radiomics based on diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D-T1) with machine learning (ML) give a most promising way to identify essential tremor (ET) at the individual level and further reveal the potential imaging biomarkers. Radiomics features were extracted from 3D-T1 and DTI in 103 ET patients and 103 age-and sex-matched healthy controls (HCs). After data dimensionality reduction and feature selection, five classifiers, including the support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP), were adopted to discriminate ET from HCs. The mean values of the area under the curve (mAUC) and accuracy were used to assess the model's performance. Furthermore, a correlation analysis was conducted between the most discriminative features and clinical tremor characteristics. All classifiers achieved good classification performance (with mAUC at 0.987, 0.984, 0.984, 0.988 and 0.981 in the test set, respectively). The most powerful discriminative features mainly located in the cerebella-thalamo-cortical (CTC) and visual pathway. Furthermore, correlation analysis revealed that some radiomics features were significantly related to the clinical tremor characteristics in ET patients. These results demonstrated that combining radiomics with ML algorithms could not only achieve high classification accuracy for identifying ET but also help us to reveal the potential brain microstructure pathogenesis in ET patients.

Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.

Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.