Integrated biomarker profiling for predicting the response of type 2 diabetes to metformin.

AIM: To explore biomarkers that can predict the response of type 2 diabetes (T2D) patients to metformin at an early stage to provide better treatment for T2D. METHODS: T2D patients with (responders) or without response (non-responders) to metformin were recruited, and their serum samples were used for metabolomic analysis to identify candidate biomarkers. Moreover, the efficacy of metformin was verified by insulin-resistant mice, and the candidate biomarkers were verified to determine the biomarkers. Five different machine learning methods were used to construct the integrated biomarker profiling (IBP) with the biomarkers to predict the response of T2D patients to metformin. RESULTS: A total of 73 responders and 63 non-responders were recruited, and 88 differential metabolites were identified in the serum samples. After being verified in mice, 19 of the 88 were considered as candidate biomarkers. Next, after metformin regulation, nine candidate biomarkers were confirmed as the biomarkers. After comparing five machine learning models, the nine biomarkers were constructed into the IBP for predicting the response of T2D patients to metformin based on the Naïve Bayes classifier, which was verified with an accuracy of 89.70%. CONCLUSIONS: The IBP composed of nine biomarkers can be used to predict the response of T2D patients to metformin, enabling clinicians to start a combined medication strategy as soon as possible if T2D patients do not respond to metformin.

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medication (ASM). In resource-limited settings, investigations are usually not available, so a clinical separation is required. We used a naïve Bayes approach to devise an algorithm to do this, and compared its accuracy with algorithms devised by five other machine learning methods. METHODS: We used data on 28 clinical variables from 503 patients attending an epilepsy clinic in India with defined epilepsy type, as determined by an epileptologist with access to clinical, imaging, and EEG data. We adopted a machine learning approach to select the most relevant variables based on mutual information, to train the model on part of the data, and then to evaluate it on the remaining data (testing set). We used a naïve Bayes approach and compared the results in the testing set with those obtained by several other machine learning algorithms by measuring sensitivity, specificity, accuracy, area under the curve, and Cohen's kappa. RESULTS: The six machine learning methods produced broadly similar results. The best naïve Bayes algorithm contained eleven variables, and its accuracy was 92.2% in determining epilepsy type (sensitivity 92.0%, specificity 92.7%). An algorithm incorporating the best eight of these variables was only slightly less accurate - 91.0% (sensitivity 89.6%, and specificity 95.1%) - and easier for clinicians to use. CONCLUSION: A clinical algorithm with eight variables is effective and accurate at separating focal from generalised epilepsy. It should be useful in resource-limited settings, by epilepsy-inexperienced doctors, to help determine epilepsy type and therefore optimal ASMs for individual patients, without the need for EEG or neuroimaging.

Employing Bayesian analysis to establish a cut-off point and assess stigma prevalence in substance use disorder: a comprehensive study of the Chinese version of the Substance Use Stigma Mechanism Scale.

PURPOSE: In China, individuals with substance use disorders (SUD) face severe stigma, but reliable stigma assessment tool is lacking. Therefore, this study aimed to validate the Chinese version of the Substance Use Stigma Mechanism Scale (SU-SMS-C) and set its cut-off point. METHODS: We recruited 1005 individuals with SUDs from Chinese rehabilitation centers. These participants completed a battery of questionnaires that included the SU-SMS-C, The Multidimensional Scale of Perceived Social Support (MSPSS), Center for Epidemiologic Studies Depression Scale (CES-D), General Self-Efficacy Scale (GSES), and Perceived Devaluation and Discrimination (PDD). Confirmatory factor analysis was used to assess the construct validity of the scale. Additionally, the Naive Bayes classifier was used to establish the cut-off point for the SU-SMS-C. We additionally explored the correlation between patient demographic characteristics and stigma. RESULTS: A confirmatory factor analysis was utilized, revealing a second-order five-factor model. Based on the Naive Bayes classifier, the area under the receiver operating characteristic (AUCROC) of 0.746, the cut-off point for the SU-SMS-C was established at 44.5. The prevalence of stigma observed in the study population was 49.05%. Significant disparities were observed in the distribution of stigma across genders, with males experiencing more pronounced stigma than females. Moreover, patients consuming different primary substances reported diverse levels of stigma. Notably, those primarily using heroin endured a higher degree of stigma than users of other substances. CONCLUSION: The study is the first to identify a cut-off point for the SU-SMS-C by Naive Bayes classifier, bridging a major gap in stigma measurement research. SU-SMS-C may help treat and manage SUDs by reducing stigma.

Evaluation of Statistical Approaches in Developing a Predictive Model of Severe COVID-19 during Early Phase of Pandemic with Limited Data Resources.

As evidence of risk factors for severe cases of coronavirus disease 2019 (COVID-19) was uncertain in early phases of the pandemic, the development of an efficient predictive model for severe cases to triage high-risk individuals represented an urgent yet challenging issue. It is crucial to select appropriate statistical models when available data and evidence are limited. This study was conducted to assess the accuracy of different statistical models in predicting severe cases using demographic data from patients with COVID-19 prior to the emergence of consequential variants. We analyzed data from 929 consecutive patients diagnosed with COVID-19 prior to March 2021, including their age, sex, body mass index, and past medical histories, and compared areas under the receiver operating characteristic curve (ROC AUC) between different statistical models. The random forest (RF) model, deep learning (DL) models with not too many neurons, and naïve Bayes model exhibited AUC measures of > 0.70 with the validation datasets. The naïve Bayes model performed the best with the AUC measures of > 0.80. The accuracies in RF were more robust with narrower distribution of AUC measures compared to those in DL. The benefit of performing feature selection with a training dataset before building models was seen in some models, but not in all models. In summary, the naïve Bayes and RF models exhibited ideal predictive performance even with limited available data. The benefit of performing feature selection before building models with limited data resources depended on machine learning methods and parameters.

Predicting students' academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes.

BACKGROUND: Dropout and poor academic performance are persistent problems in medical schools in emerging economies. Identifying at-risk students early and knowing the factors that contribute to their success would be useful for designing educational interventions. Educational Data Mining (EDM) methods can identify students at risk of poor academic progress and dropping out. The main goal of this study was to use machine learning models, Artificial Neural Networks (ANN) and Naïve Bayes (NB), to identify first year medical students that succeed academically, using sociodemographic data and academic history. METHODS: Data from seven cohorts (2011 to 2017) of admitted medical students to the National Autonomous University of Mexico (UNAM) Faculty of Medicine in Mexico City were analysed. Data from 7,976 students (2011 to 2017 cohorts) of the program were included. Information from admission diagnostic exam results, academic history, sociodemographic characteristics and family environment was used. The main dataset included 48 variables. The study followed the general knowledge discovery process: pre-processing, data analysis, and validation. Artificial Neural Networks (ANN) and Naïve Bayes (NB) models were used for data mining analysis. RESULTS: ANNs models had slightly better performance in accuracy, sensitivity, and specificity. Both models had better sensitivity when classifying regular students and better specificity when classifying irregular students. Of the 25 variables with highest predictive value in the Naïve Bayes model, percentage of correct answers in the diagnostic exam was the best variable. CONCLUSIONS: Both ANN and Naïve Bayes methods can be useful for predicting medical students' academic achievement in an undergraduate program, based on information of their prior knowledge and socio-demographic factors. Although ANN offered slightly superior results, Naïve Bayes made it possible to obtain an in-depth analysis of how the different variables influenced the model. The use of educational data mining techniques and machine learning classification techniques have potential in medical education.

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.

Naïve Bayes classifier assisted automated detection of cerebral microbleeds in susceptibility-weighted imaging brain images.

Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.

A Bayesian network perspective on neonatal pneumonia in pregnant women with diabetes mellitus.

OBJECTIVE: To predict the influencing factors of neonatal pneumonia in pregnant women with diabetes mellitus using a Bayesian network model. By examining the intricate network connections between the numerous variables given by Bayesian networks (BN), this study aims to compare the prediction effect of the Bayesian network model and to analyze the influencing factors directly associated to neonatal pneumonia. METHOD: Through the structure learning algorithms of BN, Naive Bayesian (NB), Tree Augmented Naive Bayes (TAN), and k-Dependence Bayesian Classifier (KDB), complex networks connecting variables were presented and their predictive abilities were tested. The BN model and three machine learning models computed using the R bnlean package were also compared in the data set. RESULTS: In constraint-based algorithms, three algorithms had different presentation DAGs. KDB had a better prediction effect than NB and TAN, and it achieved higher AUC compared with TAN. Among three machine learning modes, Support Vector Machine showed a accuracy rate of 91.04% and 67.88% of precision, which was lower than TAN (92.70%; 72.10%). CONCLUSION: KDB was applicable, and it can detect the dependencies between variables, identify more potential associations and track changes between variables and outcome.

Passenger flow anomaly detection in urban rail transit networks with graph convolution network-informer and Gaussian Bayes models.

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. However, they ignored the high-dimensional and complex spatial features of passenger flow and the dynamic behaviours of passengers in URTNs during anomaly detection. This article proposes a novel anomaly detection methodology based on a deep learning framework consisting of a graph convolution network (GCN)-informer model and a Gaussian naive Bayes model. The GCN-informer model is used to capture the spatial and temporal features of inbound and outbound passenger flows, and it is trained on normal datasets. The Gaussian naive Bayes model is used to construct a binary classifier for anomaly detection, and its parameters are estimated by feeding the normal and abnormal test data into the trained GCN-informer model. Experiments are conducted on a real-world URTN passenger flow dataset from Beijing. The results show that the proposed framework has superior performance compared to existing anomaly detection algorithms in detecting network-level passenger flow anomalies. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images.

BACKGROUND: Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images. METHODS: A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models. RESULTS: Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively. CONCLUSION: The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Vital sign-based detection of sepsis in neonates using machine learning.

AIM: Sepsis is a leading cause of morbidity and mortality in neonates. Early diagnosis is key but difficult due to non-specific signs. We investigate the predictive value of machine learning-assisted analysis of non-invasive, high frequency monitoring data and demographic factors to detect neonatal sepsis. METHODS: Single centre study, including a representative cohort of 325 infants (2866 hospitalisation days). Personalised event timelines including interventions and clinical findings were generated. Time-domain features from heart rate, respiratory rate and oxygen saturation values were calculated and demographic factors included. Sepsis prediction was performed using Naïve Bayes algorithm in a maximum a posteriori framework up to 24 h before clinical sepsis suspicion. RESULTS: Twenty sepsis cases were identified. Combining multiple vital signs improved algorithm performance compared to heart rate characteristics alone. This enabled a prediction of sepsis with an area under the receiver operating characteristics curve of 0.82, up to 24 h before clinical sepsis suspicion. Moreover, 10 h prior to clinical suspicion, the risk of sepsis increased 150-fold. CONCLUSION: The present algorithm using non-invasive patient data provides useful predictive value for neonatal sepsis detection. Machine learning-assisted algorithms are promising novel methods that could help individualise patient care and reduce morbidity and mortality.

Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes.

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups.

High-Level CNN and Machine Learning Methods for Speaker Recognition.

Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or "traditional" Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. A custom CNN is compared to several pre-trained nets using image inputs of spectrograms and Cepstral-temporal (MFCC) graphs. AML approach based on acoustic feature extraction, selection and multi-class classification by means of a Naïve Bayes model is also considered. Results show how a custom, less deep CNN trained on grayscale spectrogram images obtain the most accurate results, 90.15% on grayscale spectrograms and 83.17% on colored MFCC. AlexNet provides comparable results, reaching 89.28% on spectrograms and 83.43% on MFCC.The Naïve Bayes classifier provides a 87.09% accuracy and a 0.985 average AUC while being faster to train and more interpretable. Feature selection shows how F0, MFCC and voicing-related features are the most characterizing for this SR task. The high amount of training samples and the emotional content of the DEMoS dataset better reflect a real case scenario for speaker recognition, and account for the generalization power of the models.

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

Predicting post-stroke cognitive impairment using machine learning: A prospective cohort study.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is a serious complication of stroke that warrants prompt detection and management. Consequently, the development of a diagnostic prediction model holds clinical significance. OBJECTIVE: Machine learning algorithms were employed to identify crucial variables and forecast PSCI occurrence within 3-6 months following acute ischemic stroke (AIS). METHODS: A prospective study was conducted on a developed cohort (331 patients) utilizing data from the Affiliated Zhongda Hospital of Southeast University between January 2022 and August 2022, as well as an external validation cohort (66 patients) from December 2022 to January 2023. The optimal model was determined by integrating nine machine learning classification models, and personalized risk assessment was facilitated by a Shapley Additive exPlanations (SHAP) interpretation. RESULTS: Age, education, baseline National Institutes of Health Scale (NIHSS), Cerebral white matter degeneration (CWMD), Homocysteine (Hcy), and C-reactive protein (CRP) were identified as predictors of PSCI occurrence. Gaussian Naïve Bayes (GNB) model was determined to be the optimal model, surpassing other classifier models in the validation set (area under the curve [AUC]: 0.925, 95 % confidence interval [CI]: 0.861 - 0.988) and achieving the lowest Brier score. The GNB model performed well in the test sets (AUC: 0.919, accuracy: 0.864, sensitivity: 0.818, and specificity: 0.932). CONCLUSIONS: The present study involved the development of a GNB model and its elucidation through employment of the SHAP method. These findings provide compelling evidence for preventing PSCI, which could serve as a guide for high-risk patients to undertake appropriate preventive measures.

Utilizing machine learning for opportunistic screening for low BMD using CT scans of the cervical spine.

BACKGROUND: Computed Tomography (CT) scans of the cervical spine are often performed to evaluate patients for trauma and degenerative changes of the cervical spine. We hypothesized that the CT attenuation of the cervical vertebrae can be used to identify patients who should be screened for osteoporosis. METHODS: A retrospective study of 253 patients (177 training/validation and 76 test) with unenhanced CT scans of the cervical spine and Dual-energy x-ray Absorbtiometry (DXA) studies within 12 months of each other was performed. Volumetric segmentation of C1-T1, clivus, and first ribs was performed to obtain the CT attenuation of each bone. The correlations of the CT attenuations between the bones and with DXA measurements were evaluated. Univariate receiver operator characteristic (ROC) analyses, and multivariate classifiers (Random Forest (RF), XGBoost, Naïve Bayes (NB), and Support Vector Machines (SVM)) analyzing the CT attenuation of all bones, were utilized to predict patients with osteopenia/osteoporosis and femoral neck bone mineral density (BMD) T-scores <-1. RESULTS: There were positive correlations between the CT attenuation of each bone, and with the DXA measurements. A CT attenuation threshold of 305.2 Hounsfield Units (HU) at C3 had the highest accuracy (0.763, AUC=0.814) to detect femoral neck BMD T-scores ≤-1 and a CT attenuation threshold of 323.6 HU at C3 had the highest accuracy (0.774, AUC=0.843) to detect osteopenia/osteoporosis. The SVM classifier (AUC=0.756) had higher AUC than the RF (AUC=0.692, P=0.224), XGBoost (AUC=0.736; P=0.814), NB (AUC=0.622, P=0.133) and CT threshold of 305.2 HU at C3 (AUC=0.704, P=0.531) classifiers to identify patients with femoral neck BMD T-scores <-1. The SVM classifier (accuracy=0.816) was more accurate than using the CT threshold of 305.2 HU at C3 (accuracy=0.671) (McNemar's χ12=7.55, P=0.006). CONCLUSION: Opportunistic screening for low BMD can be done using cervical spine CT scans. A SVM classifier was more accurate than using the CT threshold of 305.2 HU at C3.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover.

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods.

OBJECTIVES: To establish the menstrual blood identification model based on Naïve Bayes and multivariate logistic regression methods by using specific mRNA markers in menstrual blood detection technology combined with statistical methods, and to quantitatively distinguish menstrual blood from other body fluids. METHODS: Body fluids including 86 menstrual blood, 48 peripheral blood, 48 vaginal secretions, 24 semen and 24 saliva samples were collected. RNA of the samples was extracted and cDNA was obtained by reverse transcription. Five menstrual blood-specific markers including members of the matrix metalloproteinase (MMP) family MMP3, MMP7, MMP11, progestogens associated endometrial protein (PAEP) and stanniocalcin-1 (STC1) were amplified and analyzed by electrophoresis. The results were analyzed by Naïve Bayes and multivariate logistic regression. RESULTS: The accuracy of the classification model constructed was 88.37% by Naïve Bayes and 91.86% by multivariate logistic regression. In non-menstrual blood samples, the distinguishing accuracy of peripheral blood, saliva and semen was generally higher than 90%, while the distinguishing accuracy of vaginal secretions was lower, which were 16.67% and 33.33%, respectively. CONCLUSIONS: The mRNA detection technology combined with statistical methods can be used to establish a classification and discrimination model for menstrual blood, which can distignuish the menstrual blood and other body fluids, and quantitative description of analysis results, which has a certain application value in body fluid stain identification.

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm.

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

Predicting pre-service teachers' computational thinking skills using machine learning classifiers.

Computational thinking (CT) skills of pre-service teachers have been explored extensively, but the effectiveness of CT training has yielded mixed results in previous studies. Thus, it is necessary to identify patterns in the relationships between predictors of CT and CT skills to further support CT development. This study developed an online CT training environment as well as compared and contrasted the predictive capacity of four supervised machine learning algorithms in classifying the CT skills of pre-service teachers using log data and survey data. First, the results show that Decision Tree outperformed K-Nearest Neighbors, Logistic Regression, and Naive Bayes in predicting pre-service teachers' CT skills. Second, the participants' time spent on CT training, prior CT skills, and perceptions of difficulty regarding the learning content were the top three important predictors in this model.