K-nearest Neighbor Model Research Articles

Logistic regression vs machine learning to predict evacuation decisions in fire alarm situations

In this study we assessed logistic regression and machine learning models to explore their performance in predicting evacuation decisions and to provide readers with insights into the accuracy of these methods. We tested seven machine learning algorithms, including classification and regression tree, Naïve Bayes, K-nearest neighbours, support vector machine, random forest, extreme gradient boosting, and artificial neural network. We used data collected from 1,807 participants through web-based experiments to train and calibrate these models. The performance of each model was evaluated by area under the curve, accuracy, recall, specificity, precision, and F1-score. The results indicate that logistic regression had the highest area under the curve value (0.831), whereas extreme gradient boosting outperformed other machine learning models in terms of accuracy (0.780), specificity (0.810) and precision (0.820). K-nearest neighbours model had the greater recall (0.859) and artificial neural network the highest F1-score (0.785). The models identified that being with a close person was the most influential factor in the response to a fire alarm.

Prescriptive analytics models for vessel inspection planning in maritime transportation

Port state control (PSC) inspections are crucial for maritime safety and pollution reduction. The inspection process involves identifying high-risk vessels, allocating surveyors, and conducting onboard checks. This study aims to optimize the selection and assignment process through a two-stage framework, balancing the benefits of identifying deficiencies against the costs of inspection delays. Initially, we employ a predict-then-optimize approach, predicting the number of vessel deficiencies using a k-nearest neighbor (kNN) model, which informs the inspection decisions. However, due to the nonlinear nature of the optimization in relation to predicted values, we also explore an estimate-then-optimize framework that estimates distributions of potential deficiencies. We enhance two prescriptive analytics models and introduce an advanced global model with a pre-processing algorithm for better distribution estimation. A case study using data from the Hong Kong port demonstrates that the estimate-then-optimize models surpass the predict-then-optimize approach, offering solutions closer to the optimal policy. Furthermore, our improved model outperforms existing methods, proving more effective in practical applications.

New models for estimating minimum miscibility pressure of pure and impure carbon dioxide using artificial intelligence techniques

CO2 miscible flooding stands out as one of the most-commonly employed and promising processes in enhanced oil recovery. The key factor for ensuring success in designing and executing CO2 injection projects lies in the precise determination of the Minimum Miscibility Pressure (MMP). Despite significant research endeavors in measuring and predicting the MMP through the use of empirical, analytical, numerical, and experimental approaches, the need for a comprehensive, accurate, efficient, and easy-to-use model remains. In response to this gap, our study developed various predictive models with 205 datasets, including well-established ones such as Artificial Neural Network (ANN), Random Forest (RF), and K-Nearest Neighbors (KNN) along with employing the Gradient Boosting Algorithm (GBA) with the Grid Search (GS) as a novel approach to predict the MMP of both pure and impure CO2. The performance of these models was validated by comparing them with previously published models, using relevant performance metrics such as R2 (coefficient of determination) and MAPE (mean absolute percentage error). Our results demonstrated that the GBA model with GS optimization exhibited remarkable superiority in terms of performance and accuracy in both scenarios, outperforming previously published models as well as the developed ANN, RF, and KNN models. For pure CO2, it achieved an impressive R2 value of 0.999 on the training set and a relatively high value of 0.910 on the validation set. Additionally, the model showed remarkably low MAPE values of 0.0705 % on the training set and 9.238 % on the validation set. Similarly, for impure CO2, the GBA model with GS optimization achieved an R2 value of 0.999 on the training set and a relatively high value of 0.865 on the validation set. The model also exhibited low MAPE values of 1.66 % on the training set and 6.798 % on the validation set. These findings highlight the potential of the GBA model with GS optimization as a valuable approach for accurately estimating the MMP, offering a cost-effective and efficient approach for designing and optimizing CO2 flooding operations in enhanced oil recovery processes.

Efficient plant leaf detection through machine learning approach based on corn leaf image classification

Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the improved k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

<p>Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.</p>

Detection of COVID-19 Disease with Machine Learning Algorithms from CT Images

COVID-19, caused by the SARS-COV-2 virus, which has killed more than 6 million people, is one of the most contagious diseases in human history. It has seriously affected every area that people come into contact with, from business life to economy, from transportation to education, from social life to psychology. Although the developed vaccines provide a partial decrease in the number of deaths, the mutations that the virus constantly undergoes and the increase in the transmission rate accordingly reduce the effectiveness of the vaccines, and the number of deaths tends to increase as the number of infected people. It is undoubtedly important that the detection of this epidemic disease, which is the biggest crisis that humanity has experienced in the last century after World War II, is carried out accurately and quickly. In this study, a machine learning-based artificial intelligence method has been proposed for the detection of COVID-19 from computed tomography images. The features of images with two classes are extracted using the Local Binary Pattern. The images reserved for training in the dataset were used for training machine learning models. Trained models were tested with previously unused test images. While the Fine K-Nearest Neighbors model reached the highest accuracy with a value of 0.984 for the training images, the highest accuracy value was obtained by the Cubic Support Vector Machine with 0.93 for the test images. These results are higher than the deep learning-based study using the same data set.

Performance analysis of data-driven and physics-informed machine learning methods for thermal-hydraulic processes in Full-scale Emplacement experiment

This study centers on the critical aspect of thermal management in deep geological repositories, particularly concerning the heat induced by radioactive nuclear waste. It evaluates eight machine learning methods to analyze thermal-hydraulic processes, focusing on their accuracy in predicting temperature and relative humidity, computational efficiency, robustness, and sensitivity to parameters. These methods are applied to data from the Full-scale Emplacement experiment in the Mont Terri underground laboratory. Notably, the Physics-Informed Machine Learning approach incorporates the experiment's heater power output conditions. The results demonstrate the effectiveness of these methods, especially the Physics-Informed Machine Learning model, in simulating complex processes typical of deep geological repository systems. The study highlights the k-Nearest Neighbor model's efficacy due to its proximity-based decision-making and the superior performance of the Physics-Informed model, suggesting its broader applicability. Our findings offer valuable insights for enhancing the design, management, and safety of underground facilities, contributing novel tools for precision and computational efficiency in energy process simulations.

Quaia, the Gaia-unWISE Quasar Catalog: An All-sky Spectroscopic Quasar Sample

We present a new, all-sky quasar catalog, Quaia, that samples the largest comoving volume of any existing spectroscopic quasar sample. The catalog draws on the 6,649,162 quasar candidates identified by the Gaia mission that have redshift estimates from the space observatory’s low-resolution blue photometer/red photometer spectra. This initial sample is highly homogeneous and complete, but has low purity, and 18% of even the bright (G < 20.0) confirmed quasars have discrepant redshift estimates (∣Δz/(1 + z)∣ > 0.2) compared to those from the Sloan Digital Sky Survey (SDSS). In this work, we combine the Gaia candidates with unWISE infrared data (based on the Wide-field Infrared Survey Explorer survey) to construct a catalog useful for cosmological and astrophysical quasar studies. We apply cuts based on proper motions and colors, reducing the number of contaminants by approximately four times. We improve the redshifts by training a k-Nearest Neighbor model on SDSS redshifts, and achieve estimates on the G < 20.0 sample with only 6% (10%) catastrophic errors with ∣Δz/(1 + z)∣ > 0.2 (0.1), a reduction of approximately three times (approximately two times) compared to the Gaia redshifts. The final catalog has 1,295,502 quasars with G < 20.5, and 755,850 candidates in an even cleaner G < 20.0 sample, with accompanying rigorous selection function models. We compare Quaia to existing quasar catalogs, showing that its large effective volume makes it a highly competitive sample for cosmological large-scale structure analyses. The catalog is publicly available at 10.5281/zenodo.10403370.

Performance Evaluation of Machine Learning Model in Predicting Heart Disease Prevalent

Heart disease stands as the foremost contributor to global mortality in the present era. While a range of medical tests aids in diagnosing various manifestations of heart disease, anticipating its occurrence without resorting to these tests presents a formidable challenge. Machine learning has revealed a potential path for comprehending extensive medical datasets, unearthing concealed insights that elude human observation. Random Forest, Logistic Regression, XG Boost, k-nearest neighbor, and Gradient boosting was used to predict instances of heart diseases based on age and gender in this study. Regardless of the dataset's generalization or gender-specific characteristics, we observed that Random Forest, XG Boost, and K-Nearest Neighbors models exhibit high robustness and minimal sensitivity to hyperparameters. While the prediction accuracy remains consistent among Random Forest, Xgboost, and K-Nearest Neighbors, the crucial features responsible for achieving that accuracy vary among them. This observation indicates that it is important to test different machine learning algorithms when the interest is in prediction and understanding the most important feature(s). Keywords: Machine Learning, Heart Disease, Diagnosis, Predictions, Accuracy CISDI Journal Reference Format Abiola O. A. Adebisi R. O. Adeyemo A. B. (2024): Performance Evaluation of Machine Learning Model in Predicting Heart Disease Prevalent. Computing, Information Systems, Development Informatics &amp; Allied Research Journal. Vol 15 No 1, Pp 63-72.dx.doi.org/10.22624/AIMS/CISDI/V15N1P5. Available online at www.isteams.net/cisdijournal

Qingxiangxing Baijiu sensory quality grade classification by 1H NMR and GC combined with multivariate statistical analysis

The traditional uniform artificial sensory evaluation makes it difficult to standardize the classification of different sensory quality grades of Baijiu. In this study, a total of 92 authentic Qingxiangxing Baijiu samples with 3 sensory quality grades were carefully collected. Gas chromatography (GC) was used to determine 46 main flavor components and proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to obtain hydrogen atom characteristic information of organic compounds. The principal component analysis (PCA), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models were conducted and fully validated by internal leave-one-out cross validation (LOOCV) and external repeated double random cross validation (RDRCV). The sensory quality grades of Qingxiangxing Baijiu were effectively classified by using GC and 1H NMR techniques coupled with PCA/KNN analysis with the averaged accuracy higher than 80%. In addition, synthetic minority oversampling technique (SMOTE) algorithm was successfully used to address the model overfitting problem caused by an unbalanced sample composition. This study demonstrated that 1H NMR and GC combined with multivariate statistical analysis were effective for sensory quality classification of Qingxiangxing Baijiu.

Predicting Student Placement in College Using Machine Learning

Abstract: The research on student placement prediction in higher education has been a focal point, addressing challenges related to balanced accuracy and generalization across diverse datasets. Prior studies grappled with issues in feature representation and interpretability. This study aims to overcome these challenges by introducing a comprehensive machine learning framework for student placement prediction, leveraging advanced techniques in exploratory data analysis, preprocessing, and model evaluation. Drawing on previous research experiences, our proposed work targets specific issues related to feature engineering, categorical variable representation, and result interpretability. The methodology employs key libraries, including NumPy, pandas, Matplotlib, Seaborn, Plotly, scikit-learn, WordCloud, and DateTime, for efficient data manipulation, visualization, and analysis. Ensemble learning techniques, such as Random Forest and XGBoost, along with traditional algorithms like Decision Trees and K-Nearest Neighbors, contribute to enhancing predictive accuracy and model robustness. To fine-tune the models, a Randomized Search for Hyperparameters is implemented for the XGBoost classifier, optimizing parameters like learning rate, maximum depth, minimum child weight, gamma, and colsample by tree. This approach effectively addresses overfitting and underfitting issues, maximizing overall model performance. The accuracy percentages achieved through our models represent significant advancements. For instance, the Decision Tree model achieves an accuracy of 87.74%, the Random Forest model achieves 87.60%, the XGBoost model achieves 87.60%, and the K-Nearest Neighbors model achieves 85.18%. These results underscore the effectiveness of our approach in achieving high accuracy while maintaining interpretability.

Efficient improvement of energy detection technique in cognitive radio networks using K-nearest neighbour (KNN) algorithm

With the birth of the IoT era, it is evident that the existing number of devices is going to rise exponentially. Any two devices will communicate with each other using the same frequency band with limited availability. Therefore, it is of vital importance that this frequency band used for communication be used efficiently to accommodate the maximum number of devices with the available radio resources. Cognitive radio (CR) technology serves this exact purpose. The stated one is an intelligent radio that is made to automatically identify the optimal wireless channel in the available wireless spectrum at a given instant. An important functionality of CR is spectrum sensing. Energy detection is a very popular algorithm used for spectrum sensing in CR technology for efficient allocation of radio resources to the devices intended to communicate with each other. Energy detection detects the presence of a primary user (PU) signal by continuously monitoring a selected frequency bandwidth. The conventional energy detection technique is known to perform poorly in lower SNR ranges. This paper works towards the improvement of the energy detection algorithm with the help of machine learning (ML). The ML model uses the general properties of the signal as training data and classifies between a PU signal and noise at very low SNR ranges (− 25 to − 10 dB). In this research, a K-nearest neighbours (KNN) model is selected for its versatility and simplicity. Upon testing the model with an out-of-sample dataset, the KNN model produced a detection accuracy of 94.5%.

Enhanced fault detection in polymer electrolyte fuel cells via integral analysis and machine learning

The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma.

To evaluate the value of a machine learning model using enhanced CT radiomics features in the prediction of lymphovascular invasion (LVI) of esophageal squamous cell carcinoma (ESCC) before treatment. We reviewed and analyzed the enhanced CT images of 258 ESCC patients from June 2017 to December 2019. We randomly assigned the patients in a ratio of 7:3 to a training set (182 cases) and a validation (76 cases) set. Clinical risk factors and CT image characteristics were recorded, and multifactor logistic regression was used to screen independent risk factors of LVI of ESCC patients. We extracted the CT radiomics features using the FAE software and screened radiomics features using maximum relevance and minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, and finally, the radiomics labels of each patient were established. Five machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), Gauss naive Bayes (GNB), and multilayer perceptron (MLP), were used to construct the model of radiomics labels, and its clinical features were screened. The predictive efficacy of the machine learning model for LVI of ESCC was evaluated using the receiver operating characteristic (ROC) curve. Tumor thickness [OR = 1.189, 95% confidence interval (CI) 1.060-1.351, P = 0.005], tumor-to-normal wall enhancement ratio (TNR) (OR = 2.966, 95% CI 1.174-7.894, P = 0.024), and clinical N stage (OR = 5.828, 95% CI 1.752-20.811, P= 0.005) were determined as independent risk factors of LVI. We extracted 1,316 features from preoperative enhanced CT images and selected 14 radiomics features using MRMR and LASSO to construct the radiomics labels. In the test set, SVM, KNN, LR, and GNB showed high predictive performance, while the MLP model had poor performance. In the training set, the area under the curve (AUC) values were 0.945 and 0.905 in the KNN and SVM models, but these decreased to 0.866 and 0.867 in the validation set, indicating significant overfitting. The GNB and LR models had AUC values of 0.905 and 0.911 in the training set and 0.900 and 0.893 in the validation set, with stable performance and good fitting and predictive ability. The MLP model had AUC values of 0.658 and 0.674 in the training and validation sets, indicating poor performance. A multiscale combined model constructed using multivariate logistic regression has an AUC of 0.911 (0.870-0.951) and 0.893 (0.840-0.962), accuracy of 84.4% and 79.7%, sensitivity of 90.8% and 87.1%, and specificity of 80.5% and 79.0% in the training and validation sets, respectively. Machine learning models can preoperatively predict the condition of LVI effectively in patients with ESCC based on enhanced CT radiomics features. The GNB and LR models exhibit good stability and may bring a new way for the non-invasive prediction of LVI condition in ESCC patients before treatment.

A machine learning approach to predict the efficiency of corrosion inhibition by natural product-based organic inhibitors

This paper presents a quantitative structure–property relationship (QSPR)-based machine learning (ML) framework designed for predicting corrosion inhibition efficiency (CIE) values in natural organic inhibitor compounds. The modeling dataset comprises 50 natural organic compounds, with 11 quantum chemical properties (QCP) serving as input features, and the target variable being the corrosion inhibition efficiency (CIE) value. To enhance the predictive accuracy of the ML model, the kernel density estimation (KDE) function is employed to generate virtual samples during the training process, with the overarching goal of refining the precision of the ML model. Three distinct models, namely random forest (RF), gradient boosting (GB), and k-nearest neighbor (KNN), are tested in the study. The results demonstrate a noteworthy enhancement in the prediction performance of the models, attributable to the incorporation of virtual samples that effectively improve the correlation between input features and target values. Consequently, the accuracy of the predicted CIE values is significantly augmented, aligning more closely with the actual CIE values. Performance improvements were evident across all models after the incorporation of virtual samples. The GB, RF, and KNN models exhibited increments in R2 values from 0.557 to 0.996, 0.522 to 0.999, and 0.415 to 0.994, respectively, concomitant with the introduction of 500 virtual samples. Additionally, each model demonstrated a notable reduction in RMSE values, transitioning from 1.41 to 0.19, 1.27 to 0.10, and 1.22 to 0.16, respectively. While the GB model initially outperformed others before the addition of virtual samples, the performance of the model exhibited fluctuation as the number of virtual samples varied. This behavior suggests that the KDE function provides a certain level of resilience against model variations. The proposed approach contributes to the effective design and exploration of corrosion inhibitor candidates, offering a reliable and accurate predictive tool that bridges the gap between theoretical studies and experimental synthesis.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

ABSTRACT With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models.

High-temperature effect on the mechanical behavior of recycled fiber-reinforced concrete containing volcanic pumice powder: An experimental assessment combined with machine learning (ML)-based prediction

The increasing volume of waste generated by various activities has increased interest in using waste to create sustainable construction materials to achieve possible benefits. In addition, using recycled materials to produce fresh concrete is a desirable option because of its low cost, lower landfill space requirement, and the completed concrete quality. Therefore, an experimental inquiry is undertaken to ascertain the impacts of up to 20 wt% cement displaced by Volcanic Pumice Powder (VPP) with the incorporation of 1% and 2% Recycled Nylon Fiber (RNF) on the mechanical properties of concrete composites following room temperature to high-temperature (600 °C) exposure. Fresh concrete characteristics tests were performed, including slump, compacting factor, Kelly ball penetration, and density. The heat resistance of the concrete was then measured by calculating the percentage decrease in weight, the splitting tensile strength, and the compressive strength of the specimens. Heating mainly raised VPP's pozzolanic reactivity and lowered high vapor pressure through melting RNF. Therefore, VPP and RNF-treated concrete had superior mechanical performance than control concrete even when exposed to elevated temperatures. Further, the microstructural modifications brought on by RNF and VPP additions were also explored by deploying Scanning Electron Microscopy (SEM). The use of VPP in concrete led to an improvement in fresh properties, while RNF demonstrated deterioration in the same qualities. Despite this, supervised machine learning techniques are a central focus of this investigation because of their potential to predict concrete characteristics accurately. To predict the fresh and mechanical characteristics of concrete, both the Random Forest (RF) and the K-Nearest Neighbors (KNN) algorithm, along with their ensemble model counterparts, were explored. The outcomes revealed that RNF and VPP considerably improved the concrete's heat resilience and mechanical characteristics and halted the concrete composites' explosive spalling behavior at 600 °C temperatures. To prevent strength loss at high temperatures, it was discovered that adding 1% RNF content to concrete with 10% VPP was the best combination. In addition, the high coefficient correlation (R2) value of the RF and the ensemble model indicates great accuracy in outcome prediction, while the low R2 value of the KNN model indicates that the KNN model is less accurate.

Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches.

The increasing prevalence of nonalcoholic fatty liver disease (NAFLD) in China presents a significant public health concern. Traditional ultrasound, commonly used for fatty liver screening, often lacks the ability to accurately quantify steatosis, leading to insufficient follow-up for patients with moderate-to-severe steatosis. Transient elastography (TE) provides a more quantitative diagnosis of steatosis and fibrosis, closely aligning with biopsy results. Moreover, machine learning (ML) technology holds promise for developing more precise diagnostic models for NAFLD using a variety of laboratory indicators. This study aims to develop a novel ML-based diagnostic model leveraging TE results for staging hepatic steatosis. The objective was to streamline the model's input features, creating a cost-effective and user-friendly tool to distinguish patients with NAFLD requiring follow-up. This innovative approach merges TE and ML to enhance diagnostic accuracy and efficiency in NAFLD assessment. The study involved a comprehensive analysis of health examination records from Suzhou Municipal Hospital, spanning from March to May 2023. Patient data and questionnaire responses were meticulously inputted into Microsoft Excel 2019, followed by thorough data cleaning and model development using Python 3.7, with libraries scikit-learn and numpy to ensure data accuracy. A cohort comprising 978 residents with complete medical records and TE results was included for analysis. Various classification models, including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost), were constructed and evaluated based on the area under the receiver operating characteristic curve (AUROC). Among the 916 patients included in the study, 273 were diagnosed with moderate-to-severe NAFLD. The concordance rate between traditional ultrasound and TE for detecting moderate-to-severe NAFLD was 84.6% (231/273). The AUROC values for the RF, LightGBM, XGBoost, SVM, KNN, and LR models were 0.91, 0.86, 0.83, 0.88, 0.77, and 0.81, respectively. These models achieved accuracy rates of 84%, 81%, 78%, 81%, 76%, and 77%, respectively. Notably, the RF model exhibited the best performance. A simplified RF model was developed with an AUROC of 0.88, featuring 62% sensitivity and 90% specificity. This simplified model used 6 key features: waist circumference, BMI, fasting plasma glucose, uric acid, total bilirubin, and high-sensitivity C-reactive protein. This approach offers a cost-effective and user-friendly tool while streamlining feature acquisition for training purposes. The study introduces a groundbreaking, cost-effective ML algorithm that leverages health examination data for identifying moderate-to-severe NAFLD. This model has the potential to significantly impact public health by enabling targeted investigations and interventions for NAFLD. By integrating TE and ML technologies, the study showcases innovative approaches to advancing NAFLD diagnostics.

EEG-based machine learning models for the prediction of phenoconversion time and subtype in isolated rapid eye movement sleep behavior disorder.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index, and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. A total of 236 iRBD patients were followed up for up to 8 years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. Slowing of the EEG was an important feature for both models. Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.