Classification Tree Research Articles

Handwriting Recognition Implementation: A Machine Learning Approach

Handwritten text recognition, also referred to as handwritten character recognition, is a field of study that combines model recognition, computer vision, and artificial intelligence. In order to translate handwritten letters into relevant text and computer commands in real time, handwriting recognition systems use pattern matching. The properties of photographs and touch-screen devices can be acquired, detected, and converted into a machine-readable form by an algorithm that recognizes handwriting. An ensemble of bagged classification trees is one way to accomplish this. A bagged classification tree is an ensemble learning technique that helps to increase the efficiency and accuracy of machine learning algorithms by lowering the variance of a prediction model and addressing bias-variance trade-offs. The standard Kaggle digits dataset from (0-9) was utilised in this study to identify handwritten digits using a bagged classification method. And with an accuracy level of 0.8371, we finally came to a conclusion about the importance of the bagged classification strategy.

Evaluating Dimensionality Reduction Techniques in Bitcoin Ransomware Detection: Comparative Analysis of Incremental PCA and UMAP

ABSTRACTThis study investigates the classification performance of various machine learning algorithms on the Bitcoin Heist ransomware dataset, focusing on the effects of dimensionality reduction techniques. The primary objective was to evaluate the classifiers' effectiveness in distinguishing between malicious and benign transactions under three experimental scenarios: without dimensionality reduction, utilizing Incremental Principal Component Analysis (IPCA), and applying Uniform Manifold Approximation and Projection (UMAP). The methodology involved rigorous experimentation with four classifiers: K‐Nearest Neighbors (KNN), XGBoost, Decision Tree, and Multi‐Layer Perceptron (MLP). The results demonstrated that dimensionality reduction techniques, particularly UMAP, improved the performance of KNN and Decision Tree classifiers while adversely affecting the performance of XGBoost and MLP. Notably, KNN consistently outperformed the other classifiers across different scenarios, indicating its robustness in handling reduced feature spaces. This study concludes that the effectiveness of dimensionality reduction is contingent upon the specific characteristics of the classifiers employed.

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RADS) is combined with clinical variables predominantly based on logistic regression models. This study explores modeling using regularization techniques such as ridge regression, LASSO, elastic net, classification tree, tree ensemble models like random forest or XGBoost, and neural networks to predict CsPCa in a dataset of 4799 patients in Catalonia (Spain). An 80-20% split was employed for training and validation. We used predictor variables such as age, prostate-specific antigen (PSA), prostate volume, PSA density (PSAD), digital rectal exam (DRE) findings, family history of PCa, a previous negative biopsy, and PI-RADS categories. When considering a sensitivity of 0.9, in the validation set, the XGBoost model outperforms others with a specificity of 0.640, followed closely by random forest (0.638), neural network (0.634), and logistic regression (0.620). In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. Using SHAP values for model explainability, PI-RADS emerges as the most influential risk factor, particularly for individuals with PI-RADS 4 and 5. Additionally, a positive digital rectal examination (DRE) or family history of prostate cancer proves highly influential for certain individuals, while a previous negative biopsy serves as a protective factor for others.

A Pilot Study on Video Game Training Effects on Visual Working Memory: Behavioral and Neural Insights

Background/Objectives: Recent research suggests that video games may serve as cognitive training tools to enhance visual working memory (VWM) capacity. However, the effectiveness of game-based cognitive training remains debated, and the underlying neural mechanisms, as well as the relationship between training efficacy and game design factors, are unclear. This study aimed to evaluate the impact of video game training on VWM capacity and explore its neural correlates. Methods: Two groups underwent 56 daily 20 min training sessions with two distinct video games targeting different cognitive skills: a reaction-time training game and a VWM-specific training game. Behavioral assessments included accuracy, hit response times, correct rejection response times, and Cowan’s K values. Neural correlates were measured through Negative Slow Wave (NSW) activity using EEG. Decision tree classification analyses were applied to NSW data across sessions and set sizes to identify patterns linked to VWM capacity. Results: Preliminary results are that both groups showed improvements in behavioral measures (accuracy, response times, and Cowan’s K values). NSW analyses revealed a main effect of set size in both groups, and classification results indicated that NSW patterns differed between groups, across sessions, and set sizes, supporting the relationship between NSW and VWM capacity. Conclusions: These findings contribute to understanding NSW as a neurophysiological correlate of VWM capacity, demonstrating its plasticity through video game training. Simple video games could effectively enhance behavioral and neural aspects of VWM, encouraging their potential as accessible cognitive training tools.

Development and validation of a prediction model for bronchopulmonary dysplasia using respiratory severity score.

To develop and validate a prediction model for severe bronchopulmonary dysplasia (BPD) that integrates the respiratory severity (RS) score with early postnatal risk factors. This retrospective cohort study included preterm infants born at less than 32 weeks gestation or with a birth weight of less than 1500 g, from Aichi Prefecture (training dataset) and Saitama Medical University (validation dataset) from April 1, 2016, to March 31, 2020. The primary outcome was severe BPD, defined as the use of home oxygen therapy or death due to BPD. We used classification and regression tree (CART) analysis to explore the relationship between outcomes and BPD risk factors in the training dataset. The incidence of severe BPD was 149 out of 2026 (7.3%) in the training dataset and 35 out of 387 (8.9%) in the validation dataset. CART analysis identified gestational age and the RS score as significant predictors of outcome in the day 7 and day 14 models, with C-statistics of 0.789 and 0.779, respectively. When applied to the validation dataset, these models achieved C-statistics of 0.753 and 0.827, respectively. Our prediction models demonstrated the ability to predict severe BPD, with the RS score being a crucial predictor. Many existing prediction models for bronchopulmonary dysplasia (BPD) use multiple predictors, and do not provide specific cutoff values, which complicates their clinical application. To address this issue, we developed a prediction model for severe BPD based on a score derived from mean airway pressure and inhaled oxygen concentration at 1-2 weeks of age. This user-friendly model can be easily integrated into clinical practice, facilitating treatment decisions based on predicted probabilities.

Main characteristics of French farms adopting cereal–legume intercropping: A quantitative exploration at the national and local levels

CONTEXTCereal–legume intercropping is a diversification practice that offers many advantages, especially in low-input systems. However, its adoption remains low on European farms, as technical and economic barriers hinder its development. In recent years, an increase in the proportion of arable land cultivated with cereal–legume intercrops has been observed in France. Three areas in particular – in Western, Eastern and Southern France – seem to be particularly dynamic. OBJECTIVEThis study aimed (i) to identify the main farm characteristics associated with the presence of cereal–legume intercrops at the national level in France and (ii) to highlight more specific characteristics that could explain the particular dynamics observed in each focus region. METHODSWe analysed data from the 2020 French Agricultural Census for 43,968 farms representative of the French arable crop, livestock, and mixed crop–livestock farming systems. Through a literature review, we identified key factors linked to the presence of cereal–legume intercrops and related them to 42 variables in the census. At the national level, the most important of these variables were identified and interpreted using a balanced random forest and a classification and regression tree (CART). We tested the CART obtained at the national level in the Western, Eastern, and Southern areas and conducted a random forest analysis for each area to identify local particularities. RESULTS AND CONCLUSIONAt the national level, the presence of cereal–legume intercropping was strongly linked to organic farming and the presence of livestock, especially ruminants. These intercrops were prevalent on farms with high feed autonomy for the cattle and sheep. Additionally, they were commonly observed on farms with grain storage, possibly indicating feed autonomy, on-farm transformation, or marketing outside of agricultural cooperatives. The belonging to a farm machinery cooperative was also strongly associated with cereal–legume intercropping, likely because these cooperatives give farmers access to specific machinery and provide opportunities for knowledge exchange regarding their practices. Similar characteristics were identified at the local level; organic farming was pivotal in the Western and Eastern areas, followed by feed autonomy for cattle. In the Southern area, however, on-farm grain storage capacity was dominant, likely due to longstanding efforts to achieve feed autonomy. SIGNIFICANCEThis exhaustive study on French farms identified key farm characteristics strongly linked to cereal–legume intercrops adoption. This insight is critical for promoting this practice, whether through national public policies or local farming support services. The methodology proposed can be easily reproduced to investigate other farming practices at different spatial scales.

Intelligent Strategic Planning Method based Algorithm (ISPMA) for Estimation of Soccer Sports Match Outcome

Estimating the soccer match outcome with adequate accuracy is still one of the biggest challenges in the sports domain. In this work, the proposed novel Intelligent Strategic Planning Method based Algorithm (ISPMA) carries out dynamic estimation of soccer team performance in terms of the match outcome and noticeably outperforms the existing state of the art methods due to its unique features. In this work, the output of the four feature selection machine learning techniques i.e. Pearson correlation, forward selection, Extra tree classifier, and CHI-square is firstly unified before feeding these selected features as an input to the seven classifiers i.e. SVM (Support Vector Machine), Naïve Bayes, KNN (K-Nearest Neighbor.), Decision Tree, Random Forest, Logistic Regression, and AdaBoost. The dataset comprises eleven seasons of the English premier league and 3762 matches have been used to train the model and 418 matches to test the same. Such a reasonable size of soccer dataset is not common in previous studies. Another unique feature of this work is the time of estimation as estimation can be done during the progression of the game based on match statistics associated with the first half of the match. The proposed method uses a novel approach by computing the average values of the selected set of features for the victory of the team to estimate match results. By using these computed average values, ISPMA generates strategic planning based suggestions for the second half of the match. The strategic planning generated by the proposed method facilitates estimating the team performance and shifting the momentum from one team to another and can assist the coach, managers, and the team in carrying out effective decision-making for better match outcome.

Predicting Short-Term Mortality After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis

ObjectiveEndovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)-based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data. MethodsThis study analyzed 169 patients who underwent EVAR to identify predictors of short-term mortality (within 3 years) using DTA. Data included 23 variables such as age, gender, nutritional status, comorbidities, and surgical details. The Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the decision tree classifier. ResultsDTA identified poor nutritional status as the most significant predictor, followed by CKD, COPD, and advanced age (octogenarian). The decision tree identified 6 terminal nodes with a risk of short-term mortality ranging from 0% to 79.7%. This model had 68.6% accuracy, 65.7% specificity, and 79.0% sensitivity. ConclusionML-based DTA is promising in predicting short-term mortality after EVAR, highlighting the need for comprehensive preoperative assessment and individualized management strategies.

Automated classification of cardiac arrhythmia using short-duration ECG signals and machine learning

Accurate detection of cardiac arrhythmias is crucial for preventing premature deaths. The current study employs a dual-stage Discrete Wavelet Transform (DWT) and a median filter to eliminate noise from ECG signals. Subsequently, ECG signals are segmented, and QRS regions are extracted for further preprocessing. The study considers five cardiac arrhythmias: normal beats, Premature Ventricular Contractions (PVC), Premature Atrial Contractions (PAC), Right Bundle Branch Block (R-BBB), and Left Bundle Branch Block (L-BBB) for classification. Nine distinct temporal features are extracted from the segmented QRS complex. These features are then applied to six different classifiers for arrhythmia classification. The classifiers' performance is evaluated using the MIT-BIH Arrhythmia Database (MIT-BIH AD). Support Vector Machine (SVM) and Ensemble Tree classifiers demonstrate superior performance in classifying the five different classes. Particularly, the Support Vector Machine classifier achieves high sensitivity (97.44%), specificity (99.36%), positive predictive value (97.44%), and accuracy (98.97%) with a Gaussian kernel. This comprehensive approach, integrating preprocessing, and feature extraction, holds promise for improving automatic cardiac arrhythmia classification in clinical trials.

IMPLEMENTASI BIG DATA ANALYTICS DALAM KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA GRADIENT-BOOSTED TREE CLASSIFIER PADA PYSPARK

This study aims to classify air quality based on PM1.0, PM2.5, and PM10 parameters using a Big Data Analytics approach with the Gradient-Boosted Tree Classifier (GBT) algorithm implemented on the PySpark framework. The dataset used was downloaded from OpenAQ, covering the period from April 14, 2021, to April 16, 2023, with a total of 1,048,154 entries, representing a large and complex volume of data. The research process includes data preprocessing to address data imbalance, dataset splitting for training and testing, and hyperparameter tuning using grid search and cross-validation to optimize model performance. By leveraging PySpark’s advantage in parallel processing of large data, the GBT model achieved an accuracy of 98.87%, precision of 99.00%, recall of 98.87%, and an F1-Score of 98.90%. This study demonstrates how Big Data Analytics can enhance efficiency and accuracy in air quality classification, contributing significantly to the development of real-time monitoring systems that support air pollution mitigation and data-driven policy-making.

P-2214. The Microbiome of Community-Dwelling Adults and Risk Factors for Colonization with Antibiotic-Resistant Bacteria in Chile

Abstract Background Intestinal colonization due to antibiotic-resistant bacteria (ARB) often precedes episodes of infection and fosters the spread of antibiotic resistance. This study explores the clinical, epidemiological, and microbiome factors associated with ARB colonization in Chilean adults. Methods A cross-sectional study of 357 community-dwelling individuals from a community in central Chile was conducted between December 2018 - May 2019, using stool samples and health questionnaires. Stool samples were cultured on selective media, followed by identification with MALDI-TOF and disc diffusion susceptibility method. Fecal microbiome assessments were conducted using 16S rRNA gene sequencing performed on Illumina MiSeq. ARB colonization was defined as phenotypic resistance of recovered isolates to extended-spectrum cephalosporins, fluoroquinolones, and/or carbapenems. Associations between colonization and clinical and epidemiological factors were analyzed through classification trees and logistic regression, while diversity and composition of microbiomes were compared between participants based on colonization status. Results No exposures were associated with the risk of ARB colonization. The microbiomes of colonized individuals featured Firmicutes, with Bacteroidetes, Actinobacteria, and Proteobacteria also present, paralleling the microbiome profile of the non-colonized individuals. However, ARB-colonized individuals had an overrepresentation of Escherichia/Shigella, Klebsiella, and Enterococcus genera. There were no differences in alpha or beta diversity by ARB colonization status. Conclusion The lack of risk factors regarding ARB colonization suggests that such colonization might occur without overt selective pressures. However, the overrepresentation of specific genera in colonized individuals could subtly indicate the influence of unrecognized selective pressures, such as environmental exposure to antibiotic residues. This scenario underscores the complexity of ARB dynamics and the need for further studies to explore potential environmental and indirect factors contributing to ARB colonization in the community setting. Disclosures All Authors: No reported disclosures

P-1270. Using Machine Learning and ICD-10 Codes to Identify Cases of Laboratory-Confirmed Influenza, 2015-2020, US Flu VE Network

Abstract Background The International Classification of Diseases (ICD-10) has unique codes for influenza. Accurate classification of influenza is necessary to calculate unbiased vaccine effectiveness and typically requires laboratory testing. We used the US Influenza Vaccine Effectiveness (Flu VE) Network to determine if outpatient influenza-positive cases and influenza-negative controls could be identified by the presence or absence of certain codes to better understand the role of ICD-10 in estimating VE.Table:Twelve most common ICD-10 codes among influenza-positive outpatients in the US Flu VE Network and their corresponding rankings and frequencies among influenza-negative patients, 2015-16 through 2019-20 influenza seasons Methods During the 2015-16 through 2019-20 influenza seasons, 5 US Flu VE sites enrolled outpatients aged ≥6 months with medically attended acute respiratory illness (MAARI) defined as self-reported new or worsening cough lasting ≤7 days and tested patients for influenza by RT-PCR. We determined the frequencies of ICD-10 codes among influenza cases and controls. We also used classification and regression trees (CART) models to evaluate ICD-10 codes as predictors of influenza test results.Figure:Classification and Regression Tree using ICD-10 codes to determine influenza testing status for outpatients in the US Flu VE Network, 2015-16 through 2019-20 influenza seasons Results Over 5 seasons in the US Flu VE, we identified 2,711 ICD-10 codes assigned to enrollments among MAARI patients. Among 11,861 patients with laboratory-confirmed influenza, we identified a total of 1,234 unique ICD-10 codes versus 2,450 ICD-10 codes among 30,622 patients who tested negative for influenza. Codes for cases and controls overlapped substantially, with Diseases of the Respiratory system (J00-J99) being the most frequent (78%) among both. Among cases and controls, J06.9 (Acute upper respiratory infection, unspecified) and R05 (Cough) were the most frequent (Table). CART analysis identified a subset of 6 optimal MAARI-associated ICD-10 codes that identified 6,112 (52%) influenza cases, and these codes were absent for 25,518 (83%) test-negative controls totaling 31,630 (74%) cases and controls (Figure A). Conclusion Our findings show the importance of laboratory-confirmed influenza outcomes for influenza VE analyses; machine learning may be useful in identifying MAARI-associated ICD-10 diagnostic codes to define patients for inclusion in test-negative VE studies. In our study, we identified a restricted list of ICD-10 codes that could be used to improve VE estimates in electronic medical record systems with access to testing data. Disclosures Huong Nguyen, PhD, MPH, CSL Seqirus: Advisor/Consultant|CSL Seqirus: Grant/Research Support|GSK: Grant/Research Support|ModernaTX: Advisor/Consultant|ModernaTX: Grant/Research Support Mary Patricia Nowalk, PhD, AstraZeneca - Icosavax: Grant/Research Support|GSK: Advisor/Consultant|Merck & Co.: Grant/Research Support|Sanofi: Grant/Research Support Arnold Monto, MD, Roche: Advisor/Consultant

P-1337. Analyzing SARS-CoV-2 Case Numbers and Clustering to Predict a Nursing Home Outbreak

Abstract Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), can spread rapidly in nursing homes (NH). Intervening to reduce spread should follow a signal predicting an imminent "outbreak". Various thresholds for declaring a COVID-19 outbreak have considered local factors, viral variants, case identification timelines and vaccination rates. We evaluated the number and interval between initial SARS-CoV-2 infections (cases) most predictive of future cases using VA Community Living Centers (CLCs) as NH analogues. Methods We captured SARS-CoV-2 PCR test results from individual NH neighborhoods (specialized wards). After an initial index case, we evaluated for additional cases over the next 28 days by R² analysis and 14 days by Classification and Regression Trees (CART) analysis. We evaluated the R2 goodness-of-fit measure from separate linear models by neighborhood to measure the predictive power of various outbreak definitions, adjusted for the number of residents in the neighborhood. Also, we used CART creating a tree-like structure to predict continuous target variables to see the effect of census and time at only 14 days. Results We included 136 CLCs housing 16,339 Veterans, mean age of 74, mostly male (96%) and white (68%), in 490 neighborhoods, representing 1,252,037 Veteran exposure days from 12/18/2021 to 06/18/2022, and 1,765 SARS-CoV-2 infections. We divided neighborhoods by small [< 10 Veterans (n=30,390)], medium [10-15 veterans (n=25,349)], and large [ >15 veterans (n=31,899)]. The most predictive outbreak definition for future COVID-19 cases over the next 14 days for our linear model analysis was: 2 cases across all neighborhoods, 4 cases for large, 2 for medium and 1 for small size neighborhoods. Our preliminary CART analysis suggested the first case was predictive of another case in 5 days, but if ≥4 occurred in 5 consecutive days, it predicted a large-scale outbreak with 10 or more cases over the next 14 days if the neighborhood is large enough to have that many cases. Conclusion Our analysis suggests when 4 SARS-CoV-2 cases cluster in five days in a large neighborhood, a large outbreak is imminent, potentially warranting aggressive control measures. However further refinement of our methods is needed to confirm this conclusion. Disclosures Yasin Abul, MD, Moderna: Grant/Research Support|Moderna, Abt, CDC: Grant/Research Support Kevin McConeghy, Pharm.D., Genentech: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Moderna: Grant/Research Support|Sanofi-Pasteur: Grant/Research Support|Seqirus: Grant/Research Support Stefan Gravenstein, MD, MPH, CDC: Advisor/Consultant|CDC: Grant/Research Support|Genentech: Advisor/Consultant|Genentech: Grant/Research Support|Genentech: Honoraria|GlaxoSmithKline: Advisor/Consultant|GlaxoSmithKline: Grant/Research Support|GlaxoSmithKline: Honoraria|Janssen: Advisor/Consultant|Janssen: Grant/Research Support|Janssen: Honoraria|Moderna: Advisor/Consultant|Moderna: Grant/Research Support|Moderna: Honoraria|NIH: Grant/Research Support|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Sanofi: Advisor/Consultant|Sanofi: Grant/Research Support|Sanofi: Honoraria|Seqirus: Advisor/Consultant

Infrared non-uniformity correction algorithm based on classification and regression tree segmentation

Infrared non-uniformity correction algorithm based on classification and regression tree segmentation

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh

BackgroundChild mortality is a reliable and significant indicator of a nation’s health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge. A deeper understanding is crucial for significantly reducing child mortality rates. Accurate predictions using machine learning models can empower authorities to implement timely interventions and raise awareness. So, the study aimed to explore the factors related to child mortality and assess the efficacy of various machine-learning models in predicting child mortality in Bangladesh.Methods and materialsAbout Forty-two thousand observations, except the missing observations, were extracted for this study from the Bangladesh Demographic and Health Survey (BDHS) data conducted in 2017-18. The survey utilized a two-stage stratified sampling method, selecting 675 enumeration areas-250 in urban settings and 425 in rural areas-resulting in effective data collection from 672 clusters and 20160 households. The Chi-square test and recursive feature elimination (RFE) are used to find the relevant risk factors of child mortality among the number of factors. Six ML-based algorithms were implemented for predicting child mortality, such as Naïve Bayes, Classification and Regression Trees, Random Forest, C5.0 Classification, Gradient Boosting Machine, and Logistic Regression. Model evaluation metrics like accuracy, specificity, sensitivity, negative predictive value, F1 score, positive predictive value, k-fold cross-validation, and area under the curve (AUC) techniques were used to evaluate the performance of the models.Results and discussionThe child mortality rate is 8.2%, according to the data. The bivariate analysis showed that the child mortality rate was higher among the children whose mothers were uneducated, impoverished, underweight, aged 35-49, and gave birth before age 20. Families’ water sources and religious connections had no statistically significant impact on child mortality. The prediction of child mortality using machine learning models is the main objective of this study. None of the machine learning models correctly classified dead occurrences. Therefore, this study conducted over-sampling and under-sampling analysis. Approximately 76727 and 6910 observations were sampled for over-sampling and under-sampling techniques, respectively. According to the findings of the over-sampling data, the Random Forest outperformed all the other models in terms of total performance based on training and testing sets, with an accuracy of seventy percent. The k-fold cross-validation approach demonstrated the Random Forest model’s superior performance, and achieved the highest AUC (0.701). On the other hand, the Gradient Boosting Machine has the highest assessment for predicting child mortality in under-sampling analysis. The k-fold cross-validation also illustrated the better performance of the Gradient Boosting Machine.ConclusionThe Gradient Boosting Machine and Random Forest produce the best predictive power for classifying child mortality and may help to ameliorate policy decision-making in this regard.

Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

BackgroundAmebiasis represents a significant global health concern. This is especially evident in developing countries, where infections are more common. The primary diagnostic method in laboratories involves the microscopy of stool samples. However, this approach can sometimes result in the misinterpretation of amebiasis as other gastroenteritis (GE) conditions. The goal of the work is to produce a machine learning (ML) model that uses laboratory findings and demographic information to automatically predict amebiasis.MethodData extracted from Jordanian electronic medical records (EMR) between 2020 and 2022 comprised 763 amebic cases and 314 nonamebic cases. Patient demographics, clinical signs, microscopic diagnoses, and leukocyte counts were used to train eight decision tree algorithms and compare their accuracy of predictions. Feature ranking and correlation methods were implemented to enhance the accuracy of classifying amebiasis from other conditions.ResultsThe primary dependent variables distinguishing amebiasis include the percentage of neutrophils, mucus presence, and the counts of red blood cells (RBCs) and white blood cells (WBCs) in stool samples. Prediction accuracy and precision ranged from 92% to 94.6% when employing decision tree classifiers including decision tree (DT), random forest (RF), XGBoost, AdaBoost, and gradient boosting (GB). However, the optimized RF model demonstrated an area under the curve (AUC) of 98% for detecting amebiasis from laboratory data, utilizing only 300 estimators with a max depth of 20. This study highlights that amebiasis is a significant health concern in Jordan, responsible for 17.22% of all gastroenteritis episodes in this study. Male sex and age were associated with higher incidence of amebiasis (P = 0.014), with over 25% of cases occurring in infants and toddlers.ConclusionsThe application of ML to EMR can accurately predict amebiasis. This finding significantly contributes to the emerging use of ML as a decision support system in parasitic disease diagnosis.Graphical

A comparison between penalized logistic regressions and classification tree approaches

. The penalized models have been established to be necessary for classifying the emerging complex, high-dimensional datasets. One of these penalized models is the “modified elastic-net” (M-Enet). The special cases of M-Enet (ridge, bridge, Lasso, and elastic-net) can be produced based on its penalty function. By using the fact that the Fisher-Scoring (FS) and Newton-Raphson (NR) algorithms use different approaches to solve numerical problems but are the same in the canonical link, in order to ease implementation, we developed an FS algorithm for the computation of the parameter estimates of the M-Enet. A simulation study of the microbiome as complex, high-dimensional data shows that each special case of the M-Enet penalty function has superiority in the sense of model accuracy, sensitivity, and feature selection technique. A comparison study is performed between the classification of binary responses using M-Enet as a parametric method and the classification tree via “GUIDE” as a non parametric method. The statistical analysis of the numerical results reveals that the special cases of M-Enet models outperform other M-Enet models and GUIDE in terms of average prediction accuracy and sensitivity, with p-values of 0. 0362 and 0. 0214, respectively. Consequently, these models exhibit superior classification performance compared to other classifier models.

Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty

ABSTRACTThe BorealDB dataset provides annual fire and timber harvesting disturbance classifications for Ontario that are derived from a collection of independently classified Landsat scenes. This study assesses the confidence of BorealDB classifications within overlapping scene margins since multiple classifications for common locations are available. For each focal point in BorealDB, the disturbance state of its four nearest spatial orthogonal neighbors were extracted and used to produce classification tree (CT) and random forest (RF) predictions of the focal class. Uncertainty was assessed as being greatest when predictions by neighboring locations or overlapping disturbance classes disagree with the focal class. The assessment found that identified locations of uncertainty within BorealDB varied with disturbance class, with fire having lower uncertainty than timber harvesting. With the results of the analysis, we recommend the inclusion of the analysis outputs and comparisons to supplement existing ensemble confidence attribute in BorealDB.

Impact of vancomycin area under the curve in early or later phase on efficacy and nephrotoxicity in patients with enterococcal bloodstream infections: a multicenter study

BackgroundThe optimal pharmacokinetic and pharmacodynamic (PK/PD) parameters of vancomycin that can improve outcomes in enterococcal infections remain controversial. To clarify the therapeutic target for this antibiotic, this study aimed to determine vancomycin PK/PD parameters associated with efficacy in the early (during 72 h) or later (after 72 h) phase of treatment and nephrotoxicity in enterococcal bloodstream infection patients.MethodsThis multicenter retrospective study reviewed medical records of patients with enterococcal bloodstream infections treated with intravenous vancomycin infusion for at least 72 h between January 2016 and March 2024 at Phramongkutklao Hospital or Nopparatrajathanee Hospital in Bangkok, and Rachaburi Hospital in Rachaburi Province, Thailand. Patients with data available on serum vancomycin concentration were analyzed. The primary outcomes were 30-day mortality and acute kidney injury. The estimates of the mean 24-h area under the curve in the first 72 h (AUC24) and in steady state (AUCss) were determined by Bayesian theorem.ResultsOverall, 201 vancomycin concentrations were measured within the first 72 h after vancomycin treatment, while 156 were in a steady state (> 72 h). According to Classification and Regression Tree analysis, vancomycin AUC at 420 mg·h/l was the PK/PD target for 30-day mortality. Results reveal that patients with AUC24 (early phase) and AUCss < 420 mg·h/l (later phase) had significantly higher 14-day, 30-day, and in-hospital mortality than AUC ≥ 420 mg·h/l groups. In addition, patients with AUC24 ≥ 420 mg·h/l in the early phase had significantly reduced microbiological failure (p = 0.004). Patients with AUC ≥ 700 mg·h/l in early and later phases had significantly increased acute kidney injury risk. In addition, patients receiving concomitant nephrotoxic drugs had an AUC cutoff value of 650 mg·h/l. Multivariate Cox regression analysis showed that vancomycin AUCss < 420 mg·h/l, unknown source of bacteremia, and acute kidney injury were significantly associated with 30-day mortality.ConclusionsAUC 420–650 mg·h/l in early and later phases was the target of vancomycin’s PK/PD in enterococcal bacteremia patients for efficacy and to prevent acute kidney injury. This study suggests close monitoring of vancomycin levels to ensure efficacy and safety.

Predict Weather with Machine Learning

Everything, from farming to air travel, in our contemporary world is dependent on the weather forecast. However, traditional methods often ignore the intrinsic uncertainty in weather patterns leading to inaccurate predictions. This paper presents a groundbreaking method of predicting weather using Bayesian deep learning models; specifically, Bayesian neural networks are employed to capture the probabilistic nature of weather events and produce more accurate and reliable forecasts. These models represent a significant milestone in foretelling strategies as they are evaluated against popular metrics and trained with archived atmospheric data. Additionally, gradient boosting classifiers and decision tree classifiers have been performed as queries for performing comparative analyses about different machine learning algorithms used for predicting occurrence of rain tomorrow or any other type of climate-related information. For enhanced interpretability, this paper has developed interactive visualization tools that allow users to interactively explore predicted weather patterns and analyze levels of uncertainty with an aim of advancing the field of meteorology through providing actionable insights for responsible decision making within weather sensitive domains.