Improvement of heart attack prediction by the feature selection methods

The accurate estimation of aboveground biomass (AGB) in rubber plantations is essential for predicting rubber production and assessing carbon storage. Multispectral sensors mounted on unmanned aerial vehicles (UAVs) can obtain high spatiotemporal resolution imagery of rubber plantations, offering significant advantages in capturing fine structural details and heterogeneity. However, most previous studies primarily focused on developing biomass estimation models for rubber using machine learning (ML) algorithms in conjunction with feature selection methods based on UAV-acquired multispectral imagery. The reliance on feature selection methods limits the model’s generalizability, robustness, and predictive accuracy. In contrast, deep learning (DL) exhibits considerable promise in extracting features from high-resolution UAV-based multispectral imagery without the need for manual selection. Nonetheless, it remains unclear whether DL can surpass traditional ML methods in improving the AGB estimation accuracy in rubber plantations. To address this, our study evaluated the performance of three ML algorithms (random forest regression, RFR; XGBoost regression, XGBR; categorical boosting regression, CatBoost) combined with feature selection techniques and a deep convolutional neural network (DCNN) using multispectral imagery obtained from UAV for the AGB estimation of rubber plantations. The results indicate that the RFR combined with a principal component analysis (PCA) for feature selection yielded the best performance (R2 = 0.81, RMSE = 11.63 t/ha, MAE = 9.27 t/ha) between the three ML algorithms. Meanwhile, the DCNN model derived from the G, R, and NIR spectral bands achieved the highest estimation accuracy (R2 = 0.89, RMSE = 6.44 t/ha, MAE = 5.72 t/ha), where it outperformed the other ML methods. Our study highlights the great potential of combining UAV-based multispectral imagery with DL techniques to improve AGB estimation in rubber plantations, offering a new perspective for estimating the physiological and biochemical growth parameters of forests.

With the intensification of coal mining in China, rock burst hazards have become increasingly frequent and serious. To comprehensively investigate the accident indicators during mining activities, identify the key triggering factors, and better prevent rockburst accidents, this study establishes a rockburst risk prediction index system containing 16 indicators in four subsystems: geological structure, mining technology, safety management, and employee situation. By using 118 cases as sample data, this study proposes to apply four feature selection methods (Boruta, PC, RF, BE) to reveal the relationship between these indicators and select the most salient indicators for risk prediction. Then four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Machine (GBM), SVM, and KNN are used to compare the effects of different feature selection methods on the prediction accuracy of risk level. The results show that Boruta performs best as a feature selection method for RF, GBM, and KNN, and the Boruta-RF model has better predictive performance than the others.

With the development of technology, large databases become more accessible thanks to automation systems that automatically keep data and allow the use of large databases in many areas. Machine learning approaches, a sub-branch of artificial intelligence, are used in making decisions about the process by analyzing the data stored in databases and converting them into information. In this paper, the body production process of the surgical (medical) mask is analyzed. As it is known, surgical masks have become a part of our lives by becoming widespread all over the world with the COVID-19 pandemic. In the surgical mask body production process, using the real data of the production factors, first of all, filtering feature selection methods and analyzes were made and the feature selection method to be used was determined. With the specified feature selection method, the factors affecting the product quality are determined. Secondly, machine learning methods were used to determine the values and value ranges of factors (features) in the production of defect-free products. The performances of the machine learning models established in the second stage were increased by feature selection analysis. In the study, together with the parameter optimizations made to machine learning algorithms, it was seen that the best algorithm to estimate the defective product rate was the Ibk algorithm with 92.3% accuracy, 91.9% F measurement and 93% AUC value. Finally, in line with the decision rules revealed in the study, it was observed that the fabric types used for the upper/middle/lower layers that make up the body part in the mask body production process greatly affect the rates of defective or defect-free products. If the rod apparatus around the nose belongs to class k, it has been determined that many masks are defective. Improvement suggestions were presented according to the application results.

Parameter importance assessment improves efficacy of machine learning methods for predicting snow avalanche sites in Leh-Manali Highway, India

P125. Development of a novel ensemble machine learning algorithm for prediction of complications and readmission after anterior cervical spinal fusion

This paper examines the enhancement of security measures for the Internet of Things (IoT) systems through the application of Machine Learning (ML) techniques. As the number of IoT devices continues to rise, ensuring their security has become increasingly critical, given that conventional methods frequently struggle to identify advanced threats. This study explores the implementation of several ML algorithms, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to identify anomalies and intrusions within IoT networks. By conducting a comprehensive review of existing research and experiments, it highlights the effectiveness of ML in enhancing IoT security, with high detection rates for various threats, including botnet attacks, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) incidents, and intrusion attempts. DoS/DDoS attacks and many types of botnets are the most devastating attacks that have been spreading for a long time, and they are still branching out in new ways against IoT networks. They can damage IoT services and prevent these services from being used by legitimate users. Therefore, securing IoT networks becomes a significant concern. The proposed model is used to increasingly monitor network traffic for any deviations from standard patterns IoT networks. This paper also stresses the necessity of utilising suitable datasets and feature selection techniques to enhance the efficacy of ML models. To train our model, we have utilized a dataset called the IoT23 dataset, which is one of the most recent datasets that has many IoT scenarios and anomalous activities. Further-more, we utilised two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), and then we compared the results of these algorithms when training our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by CFS However, for training and testing times metrics, DT performance was superior across both feature selection methods.

Performance evaluation of machine learning techniques in predicting cumulative absolute velocity

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

BackgroundDyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually.ObjectivesThis study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention.MethodsThe dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling.ResultThe study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study’s emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia.ConclusionThese results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

To analyze the effect of macular choroidal thickness (MCT) and peripapillary choroidal thickness (PPCT) on the classification of obese and healthy children by comparing the performance of the random forest (RF), support vector machine (SVM), and multilayer perceptrons (MLP) algorithms. Fifty-nine obese children and 35 healthy children aged 6 to 15 years were studied in this prospective comparative study using optical coherence tomography. MCT and PPCT were measured at distances of 500 μm, 1,000 μm, and 1,500 μm from the fovea and optic disc. Three different feature selection algorithms were used to determine the most prominent features of all extracted features. The classification efficiency of the extracted features was analyzed using the RF, SVM, and MLP algorithms, demonstrating their efficacy for distinguishing obese from healthy children. The precision and reliability of measurements were assessed using kappa analysis. The correlation feature selection algorithm produced the most successful classification results among the different feature selection methods. The most prominent features for distinguishing the obese and healthy groups from each other were PPCT temporal 500 μm, PPCT temporal 1,500 μm, PPCT nasal 1,500 μm, PPCT inferior 1,500 μm, and subfoveal MCT. The classification rates for the RF, SVM, and MLP algorithms were 98.6%, 96.8%, and 89%, respectively. Obesity has an effect on the choroidal thicknesses of children, particularly in the subfoveal region and the outer semi-circle at 1,500 μm from the optic disc head. Both the RF and SVM algorithms are effective and accurate at classifying obese and healthy children.

Recent technological advances allowed an exponential expansion of biological sequence data, and the extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge improved the understanding of the mechanisms related to several fatal diseases, e.g., Cancer and COVID-19, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine, and precision medicine. These advances benefit our society and economy, directly impacting peoples lives in various areas, such as health care, drug discovery, forensic analysis, and food analysis. Nevertheless, ML approaches applied to biological data require representative, quantitative, and informative features. Necessarily, as many ML algorithms can handle only numerical data, sequences need to be translated into a feature vector. This process, known as feature extraction, is a fundamental step for the elaboration of high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with the design and selection of suitable features. Feature engineering, ML algorithm selection, and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge, and performed manually by a human expert. To deal with this problem, we developed a new package, BioAutoML, which automatically runs an end-to-end ML pipeline. BioAutoML extracts numerical and informative features from biological sequence databases, automating feature selection, recommendation of ML algorithm(s), and tuning of hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules, (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyperparameter tuning modules). Our experimental results, assessing the relevance of our proposal, indicate robust results for different problem domains, such as SARS-CoV-2, anticancer peptides, HIV sequences, and non-coding RNAs. According to our systematic review, our proposal is innovative compared to available studies in the literature, being the first study to propose automated feature engineering and metalearning for biological sequences. BioAutoML has a high potential to significantly reduce the expertise required to use ML pipelines, aiding researchers in combating diseases, particularly in low- and middle-income countries. This initiative can provide biologists, physicians, epidemiologists, and other stakeholders with an opportunity for widespread use of these techniques to enhance the health and well-being of their communities.

Three-phase induction motors (IMs) are one of the most employed electric machines in industrial and household applications. Condition monitoring of these machines is essential to avoid unplanned maintenance and thereby enhance the availability. Artificial Intelligence (AI) technologies are emerging as an advanced tool for automating condition monitoring process to detect incipient faults at early stages. Machine Learning (ML) algorithms have been identified as a promising approach for condition monitoring of IMs and predicting maintenance to avoid failures. However, selecting the suitable ML algorithm for a given application is challenging because there is no predefined set of application-based algorithms. In addition, raw data processing and feature selection need careful attention to improve the accuracy of the results. This paper reviews supervised ML algorithms that can be used for condition monitoring of IMs and identifies their benefits and drawbacks. It then discusses how the dominant features from raw data can be selected through time domain and frequency domain analysis using the acoustic data collected from a three-phase induction motor. The study investigates classification accuracy of each ML algorithm and a procedure for selecting an algorithm based on the experimental results. Results of this study show that Support Vector Machines (SVM) algorithm outperforms other competing algorithms in condition monitoring of IMs when the dominant frequency components obtained through Fast Fourier Transform (FFT) are used as training data.

BackgroundHemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis.MethodsUsing a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score.ResultThe study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%.ConclusionML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

Machine Learning (ML) algorithms are used in many fields such as finance, education, industry, medicine and e-commerce. ML algorithms show performance differences depending on the dataset and processing steps. Choosing the right algorithm, preprocessing and postprocessing methods has great importance to achieve good results. In this paper, Random Forest Classifier, Logistic Regression Classifier, Decision Tree Classifier, Linear Discriminant Analysis, K-Nearest Neighbor Classifier and SVC methods are compared to predict mobile phone price class. The "Mobile Price Classification" dataset which is taken from Kaggle.com is used to evaluate methods. Firstly, all values at the dataset are checked to verify that there are no missing values. After that, scaling is applied to dataset in order to obtain more relevant data for ML algorithms. Then, feature selection methods which reduce the computational cost by reducing the number of inputs are performed to get meaningful features. Finally, the parameters of classification algorithms are tuned to improve the system accuracy. According to obtained results, it is seen that ANOVA f-test feature selection method is more convenient for this dataset. It gives satisfying accuracy with a minimum number of features. It is also seen that the SVC classifier has the highest test accuracy compared to other models.

Satellite-based aerosol optical property retrieval over land, especially size-related parameters, is challenging. This study proposed a novel two-stage machine learning (ML) algorithm for retrieving aerosol optical depth (AOD), Ångström exponent (AE), fine mode fraction (FMF), and fine mode AOD (FAOD)) over land using MODIS observed reflectance. The new ML algorithm consists of three steps: (1) first, all samples extracted from AERONET measurements were used to train the ML model, (2) then, to reduce the extreme estimation bias of the model, divided low-value and high-value samples were used to train low-value and high-value ML models, respectively, and (3) finally, the three ML models were integrated into the final retrieval based on the weight interpolation. Independent site network validation results show that the new ML algorithm has a Pearson correlation coefficient (R) of 0.894 (0.638, 0.661, 0.865) and root mean square error (RMSE) of 0.146 (0.258, 0.245, 0.153) for the AOD (AE, FMF, FAOD) retrieval, which significantly outperforms the validation metrics of MODIS operational products, with AOD (AE, FMF, FAOD) RMSE of 0.130-0.156 (0.536-0.569, 0.313, 0.191). The inter-comparison of aerosol products shows that the spatial patterns of AOD, AE, FMF, and FAOD of the new ML algorithm are in good agreement with those of the MODIS and POLDER products. These results illustrate that the new ML algorithm has good performance and transferability and indicate the ability of ML methods to be applied to multispectral instruments (such as MODIS) to retrieve multiple aerosol properties.