Decision Tree Model Research Articles

Prediction and feature selection of low birth weight using machine learning algorithms

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.

Cura-LLaMA: Evaluating open-source large language models question answering capability on medical domain

This paper presents the development and evaluation of Cura-LLaMA, an open-source large language model (LLM) tailored for the medical domain. Based on the TinyLlama model, Cura-LLaMA was fine-tuned using the PubMedQA dataset to enhance its ability to address complex medical queries. The models performance was compared with the original TinyLlama, focusing on its accuracy and relevance in medical question-answering tasks. Despite improvements, the study highlights challenges in using keyword detection methods for evaluation and the limitations of omitting non-essential columns during fine-tuning. The findings underscore the potential of fine-tuning open-source models for specialized applications, particularly in resource-limited settings, while pointing to the need for more sophisticated evaluation metrics and comprehensive datasets to further enhance accuracy and

Different roads take me home: the nonlinear relationship between distance and flows during China’s Spring Festival

Human mobility modelling has attracted scholarly attention from physics-based methods and social science explanatory approaches. However, there is limited knowledge of the nonlinear relationship of flows and distance in intercity mobility and regional differences in the nonlinear relationship. Focusing on China’s long-distance and large-scale mobility during the Spring Festival, this paper develops a framework to explain the nonlinear relationship. Using the Gradient Boosting Decision Tree (GBDT) model and Tencent Big Data, we find that there are three types of nonlinear relationships, namely plateau (almost zero distance decay parameter), drop (decreasing distance decay parameter) and rebound (increasing distance decay parameter after decreasing). The provincial differences also reveal that the nonlinear relationships depend on the domestic relative location and the intra-provincial urban system. This result shows that the cities in the coastal province enjoy a more inclusive spatial structure, which supports the migration from the periphery of the province. In contrast, the inland cities are concerned with embracing the migrants and settling them down.

MRI-Based Machine Learning for Prediction of Clinical Outcomes in Primary Central Nervous System Lymphoma.

A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R (p < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival (p < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL.

Data‐driven prediction of prolonged air leak after video‐assisted thoracoscopic surgery for lung cancer: Development and validation of machine‐learning‐based models using real‐world data through the ePath system

AbstractIntroductionThe reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system.MethodsAll available data were collected from patients with lung cancer who underwent video‐assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post‐surgery. Data‐driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots.ResultsTo mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross‐validation indicated improved AUROCs when utilizing key variables, even post‐imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts.ConclusionsThe data‐driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post‐video‐assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real‐world setting.

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

A Study on Prediction and Assessment of Diabetes Mellitus Based on BP Neural Network and Decision Tree Model

A Study on Prediction and Assessment of Diabetes Mellitus Based on BP Neural Network and Decision Tree Model

Insight into RecA-mediated repair of double strand breaks is provided by probing how contiguous heterology affects recombination

Homologous recombination can promote correct repair of double strand breaks (DSB) in DNA by aligning a sequence region in the broken chromosome with the corresponding sequence region in an unbroken chromosome. D-loops join the broken and unbroken chromosomes during homology testing. Previous work studied how some mismatches affect the stability of D-loops, but they did not probe whether the D-loops disrupt regions of contiguous mismatches or simply bypass them. Furthermore, previous work has not considered how the length of flanking homology affects D-loop disruption of regions of contiguous mismatches. Finally, there are conflicts about the polarity of D-loop extension. We demonstrate that with or without ATP hydrolysis invading strands with 6 contiguous mismatches and sufficient flanking homology readily form D-loops that disrupt the structure of the mismatched region and incorporate both flanking homologous regions. Unsurprisingly, the probability that D-loops will incorporate both flanking homologous regions decreases as the number of mismatched bases increases. Furthermore, though D-loops may progress through homologous regions initially and dominantly in the 5′ to 3′ direction with respect to the single strand in the broken chromosome, our results suggest that progress through contiguous mismatches proceeds dominantly in the 3′ to 5′ direction. These results may reconcile previous conflicts about the polarity of D-loop extension. Additionally, the results suggest that homology recognition is not characterized by any simple iterative decision tree model that considers each homology testing step separately. Instead, homology recognition involves collective interactions. Finally, we consider implications for DSB repair.

Real-time detection of urban gas pipeline leakage based on machine learning of IoT time-series data

China is advancing urban gas pipeline safety through the strategic deployment of methane detectors. This study introduces a systematic approach for identifying underground gas leaks, and overcoming challenges such as biogas interference and environmental factors. Abnormal methane sequences, defined by expert experience, undergo data preprocessing, including cleansing, compressing, and extracting rising segments. Feature engineering is performed on these rising segments across three dimensions: temporal, volatility, and temperature features. Different classification algorithms, including K-nearest neighbor, decision tree, and support vector machine, are employed for gas leakage recognition. The decision tree proves most effective, achieving a 92.86% recall rate for methane leakage segments. Additionally, feature importance ranking from the decision tree model highlights the maximum value of preprocessed methane concentration time series and the fitting slope of the rising segment as crucial features for methane leakage detection.

Factors and prediction model associated with resumption of social outings in stroke survivors 6 months after onset: a multicenter prospective study

ABSTRACT Objectives To identify factors associated with the resumption of social outings 6 months after stroke onset and develop a simple clinically practical prediction model. Materials and methods Participants were recruited from first-ever stroke survivors admitted to three rehabilitation wards, and resumption of social outings 6 months after stroke onset was assessed using the Japanese version of the Frenchay Activities Index. The association of physical and cognitive functions with activities of daily living at admission to the rehabilitation ward and resumption of social outings 6 months after stroke onset was examined using logistic regression and decision trees. Results Notably, 63.2% of the 57 stroke survivors who participated in this study had lower Frenchay Activities Index scores for social outings 6 months after stroke onset than before. Logistic regression analysis revealed that attention deficit and grooming on the Functional Independence Measure (FIMTM) were significantly associated with decreased social outing scores 6 months after stroke onset. A decision tree model was created to predict the resumption of social outings using the presence or absence of attention disorders and FIMTM grooming score (>2 or ≤ 2). Conclusions The results of this study suggest that attention deficit and beyond a certain level of independence in grooming (FIMTM >2) at admission to the rehabilitation ward are associated with recovery to the pre-stroke level of social outings 6 months after stroke onset. The decision tree created in this study holds promise as a simple model to predict the resumption of social outings among stroke survivors.

Dental Composite Performance Prediction Using Artificial Intelligence.

There is a need to increase the performance and longevity of dental composites and accelerate the translation of novel composites to the market. This study explores artificial intelligence (AI), specifically machine learning (ML), to predict the performance outcomes (POs) of dental composites from their composite attributes (CAs). An extensive dataset from over 200 publications was built and refined to 233 samples with 17 CAs and 7 POs. Nine ML models were evaluated for PO prediction performance using classified data, and Five ML models were evaluated for PO regression analysis. The KNN model excelled in predicting flexural modulus (FlexMod), Decision Tree model in flexural strength (FlexStr) and volumetric shrinkage (ShrinkV), and Logistic Regression and SVM models in shrinkage stress (ShrinkStr). Receiver operating characteristic area under the curve (ROC AUC) analysis confirmed these results but found that Random Forest was more effective for FlexStr and ShrinkV, suggesting the possibility of Decision Tree overfitting the data. Regression analysis revealed that the Voting Regressor was superior for FlexMod and ShrinkV predictions, while Decision Tree Regression was optimal for FlexStr and ShrinkStr. Feature importance analysis indicated TEGDMA is a key contributor to FlexMod and ShrinkV, BisGMA and UDMA to FlexStr, and depth of cure, degree of monomer-to-polymer conversion, and filler loading to ShrinkStr. There is a need to conduct a full analysis using multiple ML models because different models predict different POs better, and for a large, comprehensive dataset to train robust AI models to facilitate the prediction and optimization of composite properties and support the development of new dental materials.

Predictive Modeling of Customer Response to Marketing Campaigns

In today’s data-driven marketing landscape, predicting customer responses to marketing campaigns is essential for optimizing both engagement and Return On Investment (ROI). This study aims to develop a predictive model using a Decision Tree (DT) to identify key factors influencing customer behavior and improve campaign targeting. The methodology involves building the DT model, initially achieving an accuracy of 87.3%. However, the model faced challenges with precision and recall due to class imbalance. To address this, a resampling technique was applied, which significantly improved model performance, increasing recall from 44% to 83.1% and the F1-score from 49% to 74.2%. Key influential features identified include the recency of a customer’s purchase, their duration as a customer, and their response history to previous campaigns. This study demonstrates the practicality and interpretability of the DT model, offering actionable insights for marketing professionals seeking to enhance campaign effectiveness and customer targeting.

The Perils of Limited Key Reuse: Adaptive and Parallel Mismatch Attacks with Post-processing Against Kyber

The Module Learning With Errors (MLWE)-based Key Encapsulation Mechanism (KEM) Kyber is NIST's new standard scheme for post-quantum encryption. As a building block, Kyber uses a Chosen Plaintext Attack (CPA)-secure Public Key Encryption (PKE) scheme, referred to as Kyber.CPAPKE. In this paper we study the robustness of Kyber.CPAPKE against key mismatch attacks. We demonstrate that Kyber's security levels can be compromised if having access to a few mismatch queries of Kyber.CPAPKE, by striking a balance between the parallelization level and the cost of lattice reduction for post-processing. This highlights the imperative need to strictly prohibit key reuse in Kyber.CPAPKE. We further propose an adaptive method to enhance parallel mismatch attacks, initially proposed by Shao et al. at AsiaCCS 2024, thereby significantly reducing query complexity. This method combines the adaptive attack with post-processing via lattice reduction to retrieve the final secret key entries. Our method proves its efficacy by reducing query complexity by 14.6 % for Kyber512 and 7.5 % for Kyber768/Kyber1024. Furthermore, this approach has the potential to improve multi-value Plaintext-Checking (PC) oracle-based side-channel attacks and fault-injection attacks against Kyber itself.

Fast predesign methodology of centrifugal compressor for PEMFCs combining a physics-based loss model and an interpretable machine learning method

For boosted hydrogen fuel cells, the centrifugal compressor greatly affects the system's efficiency, stack power and costs. However, the design of the centrifugal compressor is a lengthy and high-cost process. To tackle this issue, a new method combining a physics-based loss model and a machine learning method was proposed to achieve a fast and more accurate preliminary design step. A high-precision physics-based loss model was developed and validated for data generation. Furthermore, two gradient boosting decision tree (GBDT) models with Bayesian hyperparameter turning were established to construct mapping relationships between geometric parameters and aerodynamic performance. Then, the Sobol sensitivity analysis and Shapley Additive Explanations (SHAP)-based interpretability were used to explore the impact of the geometric parameters on the aerodynamic performances. Results showed that the outlet blade angle, impeller outlet diameter, impeller outlet width, inducer shroud diameter, and inducer blade angle (at the shroud) were the five most sensitive parameters. Additionally, some practical design recommendations were extracted using SHAP-based interpretability. The isentropic efficiencies of two redesign cases with optimal geometric parameters were increased by 2.59% and 1.85% compared with the baseline impellers. The performance prediction was completed within a time of 6 s using the proposed new predesign method. This study represents a significant new attempt to advance the preliminary design methodology of centrifugal compressors.

Commuting behaviors response to living and working built environment: Dissecting interaction effects from varied supply and demand masses

Most developed megacities have experienced poly-centralization and suburbanization, leading to job-housing mismatches and negative outcomes such as increased commuting distances and frequencies. Recognizing the scarcity of structural analysis concerning the attractiveness of commuting's geographic endpoints, this study examines the diverse impact of the built environment at living and working locations on commuting flows, considering various supply-demand masses and distance levels. Utilizing a Gradient Boosting Decision Tree (GBDT) model, the study highlights the importance of job-housing ratio, POI diversity, and subway station proximity in employment locations, while informal area rates and subway proximity remain significant in residential locations. A three-dimensional analysis further indicates that achieving a perfect job-housing balance in megacities remains a dream, with each area presenting unique challenges requiring tailored solutions beyond a one-size-fits-all approach. Potential policies, such as planning large residential areas in the inner suburbs and upgrading convenient infrastructure in the outer suburbs, are proposed. Furthermore, with the uncovered distinct interaction effects of built environment on commuting behavior across various masses and distances, common perceptions related to mixed-use land and the role of informal settlement, are critically challenged. This extends our understanding of sustainable urban system design and provides references for planning policies.

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e., carbohydrates, protein, total lipid, and total sugar content) and classified based on their weighted health risks, considering their carbohydrate, lipid, and glycemic index levels. The algorithms used for modeling include multiple linear regression (MLR), K-nearest neighbors, support vector machine, random forest regression (RFR), gradient-boosted regression, decision trees (DT), and deep neural networks. The MLR model demonstrated high accuracy on the training dataset (R2=0.99, mean absolute error [MAE]=7.71kcal, and root mean squared error [RMSE]=17.89kcal) and testing dataset (R2=0.99, MAE=7.75kcal, and RMSE=18kcal) in energy estimation, indicating its effectiveness in dietary assessment. The RFR and DT models were useful in categorizing foods into low-health-risk foods, but their performance was reduced in medium and high-health-risk groups. This research contributes to developing tools that could aid in personalized dietary planning and public health interventions to mitigate diet-related health risks. PRACTICAL APPLICATION: This study applies machine learning to estimate how many calories are in food and to understand the health risks different foods might have. By investigating the fats, cholesterol, and sugars in food items listed in a public database, we can better plan diets or develop apps that help people make healthier eating choices. This work aims to improve public access to nutritional information, supporting efforts to combat diet-related diseases through educational materials and applications that guide dietary choices in various settings.

Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking

Artificial intelligence has succeeded in many different areas in recent years. Especially the use of machine learning algorithms has been very popular in all areas, including fault detection. This paper explores a case study of applying machine learning techniques and neural networks to detect ten different machinery fault conditions using publicly available data sets collected from a tachometer, two accelerometers, and a microphone. Ten different conditions were classified using machine learning algorithms. Fifty-eight different features are extracted from time and frequency by applying the Short-Time Fourier Transform to the data with the window size of 1000 samples with 50% overlap. The Support Vector Machine models provided fault classification with 99.8% accuracy using all fifty-eight features. The proposed study explores the dimensionality reduction of the extracted features. Fifty-eight features were ranked using the Decision Tree model to identify the essential features as the classifier predictors. Based on feature extraction and raking, eleven predictors were extracted leading to reduced training complexity, while achieving a high classification accuracy of 99.7% could be obtained in less than half of the training time.

Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson's disease: A data-driven decision tree model

BackgroundDetecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments. ObjectivesDetermine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups. MethodsOur retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach—decision trees—was adopted to validate and analyze the possible inclusion of other relevant clinical features. ResultsThe decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues. ConclusionsFor the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study

ObjectivesWith the emergence of new COVID-19 variants (Omicron BA.5.2.48 and B.7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing...