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Efficient prediction of coronary artery disease using machine learning algorithms with feature selection techniques

COMPUTERS & ELECTRICAL ENGINEERING 2024年 115卷

作者： Hassan, Md. Mehedi Zaman, Sadika Rahman, Md. Mushfiqur Bairagi, Anupam Kumar El-Shafai, Walid Rathore, Rajkumar Singh Gupta, Deepak Khulna Univ Comp Sci & Engn Discipline Khulna 9208 Bangladesh North Western Univ Dept Comp Sci & Engn Khulna 9100 Bangladesh Univ Dhaka Dept Stat Dhaka 1000 Bangladesh Prince Sultan Univ Comp Sci Dept Secur Engn Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt Cardiff Metropolitan Univ Cardiff Sch Sport & Hlth Sci Llandaff Campus Western Ave Cardiff CF5 2YB Wales Maharaja Agrasen Inst Technol Dept Comp Sci Engn Delhi India

In recent years, there has been a notable surge in the prevalence of cardiovascular diseases (CVD), presenting a significant global public health challenge and a leading cause of mortality worldwide. Among the myriad complications stemming from CVD, heart failure stands out as a critical concern. Addressing heart failure through surgical means poses considerable challenges. The primary objective of this research is to identify pivotal attributes linked to heart failure and employ diverse machine learning methodologies to predict its occurrence, thereby enabling early estimation of mortality rates associated with heart failure. Leveraging a heart failure dataset, we conducted comprehensive model construction using pre-processing techniques such as feature scaling and correlation analysis. The Extreme Gradient Boosting (XGBoost) method was instrumental in evaluating feature relevance, leading to the selection of two distinct datasets: the whole dataset and the XGBoost dataset. In conclusion, we employed thirteen machine learning methods to predict the occurrence of death events within these datasets. Fine-tuning hyperparameters significantly enhanced model performance. Notably, our model demonstrated exceptional performance on this dataset, achieving the highest accuracy of 85.23% with Random Forest on the whole dataset and 86.36% with Flexible Discriminant Analysis on the XGBoost dataset.

关键词： Coronary artery disease Cardiovascular health machine learning algorithms Risk assessment Feature selection Medical data analysis XGBoost SHARP analysis

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Performance evaluation of machine learning algorithms for the prediction of particle Froude number (Frn) using hyper-parameter optimizations techniques

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Shakya, Deepti Deshpande, Vishal Safari, Mir Jafar Sadegh Agarwal, Mayank Indian Inst Technol Patna Dept Comp Sci & Engn Patna India Indian Inst Technol Patna Dept Civil & Environm Engn Patna India Yasar Univ Dept Civil Engn Izmir Turkiye

The sewer system is a critical component of urban infrastructure, responsible for transporting wastewater and stormwater away from populated areas. Proper design and management of sewer systems are essential to prevent flooding, reduce environmental pollution, and ensure public health and safety. One crucial parameter in sewer system design and management is the particle Froude number (F-rn). The goal of this study is to develop a predictive algorithm that takes into account the relevant input parameters, such as volumetric sediment concentration (C-v), dimensionless grain size of particles (D-gr), the ratio of sediment median size to the hydraulic radius (d/R), pipe friction factor (lambda) to accurately predict the F-rn using an ablation study for the condition of non-deposition with clean bed data. The proposed approach is based on hyper-parameter optimization techniques, i.e., Babysitting method (BSM), GridSearchCV (GS), random search (RS), Bayesian optimization with Gaussian process (BO-GP), Bayesian optimization with tree-structures Parzen estimator (BO-TPE), and particle swarm optimization (PSO), which are applied to the four machine learning algorithms such as random forest (RF), gradient boosting (GB), K-nearest neighbor (KNN), and support vector regression (SVR). The proposed algorithms are compared with the existing algorithms in terms of coefficient of determination (R-2), root mean square error-observations standard deviation ratio (RSR), and normalized mean absolute error (NMAE) to assess the performance of the proposed algorithms. The results show that the proposed algorithms yield superior outcomes across all performance metrics. Among the proposed algorithms, GB+PSO predicted F-rn with significant accuracy and has the highest prediction accuracy (R-2 = 0.996, RSR = 0.068, and NMAE = 0.009, respectively), followed by RF+BO-GP, SVR+RS, and KNN+PSO. We have provided a comparison with the existing state-of-the-art methods and beat them. We evaluate thes

关键词： Empirical equations machine learning algorithms Optimization techniques F-rn Sewer system

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Thermal conductivity prediction of titania-water nanofluid: A case study using different machine learning algorithms

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CASE STUDIES IN THERMAL ENGINEERING 2022年 30卷

作者： Sharma, Palash Ramesh, K. Parameshwaran, R. Deshmukh, Sandip S. Birla Inst Technol & Sci Pilani Dept Mech Engn Hyderabad Campus Hyderabad 500078 India

In this study, the thermal conductivity of titania (TiO2)-water nanofluid was predicted using five separate machine learning algorithms with their unique hyperparameters and logical functions. A detailed comparative study on the TiO2-water nanofluid dataset was performed using the artificial neural network (ANN), gradient boosting regression (GBR), support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR) algorithms. A complete data intelligence analysis of 228 collected data points on different shape and size of titania (TiO2)-water nanofluid was conducted. The dataset consisted of five parameters including size, shape, volume fraction of the nanoparticles, temperature, and thermal conductivity. The shape of the nanoparticles was considered to be altogether a new parameter for predicting the thermal conductivity of the nanofluids. The mean square error (MSE) and R-square (R-2) were used to compare these models. From this comparative study of these algorithms, it was found that the gradient boosting is the best algorithm [R-train(2) = 0.99, R-test(2) = 0.99, MSE.(train) = 0.0003 and MSE.(test) = 0.0002] for thermal conductivity predictions with test and train accuracies of 99%. It was observed that, the shape of the nanoparticles could influence the thermal conductivity predictions of the nanofluids, substantially.

关键词： TiO2-Water nanofluid machine learning algorithms Thermal conductivity Shape Volume fraction Temperature

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Soft 2D tactile sensor based on fiber Bragg gratings and machine learning algorithms

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SENSORS AND ACTUATORS A-PHYSICAL 2024年 369卷

作者： Shabalov, N. Wolf, A. Kokhanovskiy, A. Dostovalov, A. Babin, S. RAS Inst Automat & Electrometry SB 1 AcadKoptyug Ave Novosibirsk 630090 Russia Novosibirsk State Univ Pirogova St 2 Novosibirsk 630090 Russia ITMO Univ Sch Phys & Engn St Petersburg 197101 Russia

Soft 2D tactile sensors are becoming increasingly important in robotics and human-machine interaction. In this paper, we propose a new approach to develop a soft tactile sensor using fiber Bragg gratings (FBGs) and machine learning algorithms. The sensor consists of a layer of silicone elastomer with embedded 192 FBGs that can detect deformations caused by point impact. The FBG responses are then processed by machine learning algorithms to measure the position and the force of impacts with the mean absolute errors of 2.1 mm and 0.34 N, respectively.

关键词： Soft tactile sensors Fiber Bragg grating machine learning algorithms

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Classification and prediction-based machine learning algorithms to predict students' low and high programming performance

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2024年第1期32卷 e22679-e22679页

作者： Durak, Aykut Bulut, Vahide Katip Celebi Univ Dept Comp Engn Software Engn TR-35000 Izmir Turkiye

The current study aims to determine the programming performance (low and high) of the students between the ages of 12-24 who receive programming education, by descriptive analysis, to determine the current situation according to various variables and to redict them with machine learning algorithms. Thus, the change in programming performance, computational identity, computational thinking perspective, programming empowerment, and programming anxiety according to various variables was examined. The performances of different algorithms were compared in estimating the low and high programming performance of these variables. The research involved 763 students who were between the ages of 12 and 24 and had received programming education. Different scales were used to collect the opinions of students who received programming education. Descriptive analyses, one-way analysis of variance (ANOVA), and machine learning algorithms were used in the analysis of the data set. Analyzes were made in Statistical Package for the Social Sciences (SPSS) and Knime software. Decision trees, k-nearest neighbors, support vector machines, random forest, Naive Bayes classifiers, and logistic regression were used in the study. Students' programming performance, computational identity, computational thinking perspective, and programming empowerment mean scores differ in terms of gender and educational level variables. Research variables do not differ statistically in terms of academic success level variables. According to the programming performance level, students' computational identity, computational thinking perspective, and programming empowerment scores differ. No significant difference was found between programming anxiety scores. Decision trees of the algorithm with the highest accuracy result according to low and high programming performance conditions are [(0.966), (0.966)], respectively. The fact that these obtained scores are above 90% can be interpreted as sufficient estimation pe

关键词： computational identity computational thinking perspective descriptives machine learning algorithms programming anxiety programming empowerment Programming performance

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Evaluation of machine learning algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems

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ENERGIES 2023年第4期16卷 1947页

作者： Klinsuwan, Thitiphat Ratiphaphongthon, Wachiraphong Wangkeeree, Rabian Wangkeeree, Rattanaporn Sirisamphanwong, Chatchai Univ Bourgogne UFR Sci & Tech F-21078 Dijon France Univ York Dept Math Heslington YO10 5DD England Fac Sci Dept Math Phitsanulok 65000 Thailand Naresuan Univ Res Ctr Acad Excellence Math Phitsanulok 65000 Thailand Naresuan Univ Fac Sci Dept Phys Smart Energy Syst Integrat Res Unit Phitsanulok 65000 Thailand

The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants' reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863.

关键词： machine learning algorithms photovoltaic systems dynamic thresholds anomaly detection

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Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan

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GEOMATICS NATURAL HAZARDS & RISK 2024年第1期15卷

作者： Al-Sheriadeh, Mohanned S. Daqdouq, Mohammad A. Jordan Univ Sci & Technol Dept Civil Engn Irbid Jordan

The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. Both were selected because they represent two climatic regimes: desert and mountainous areas. Because of a shortage of past floods location, a physical model was utilized to generate them based on simulations of 100-year rainfall. 10,000 of them were selected randomly and used for MLAs training and testing. During training, thirteen flood influential factors were identified. Out of them, the distance to stream, elevation, and topographic wetness index have shown an overwhelming effect in Zarqa Ma'in watershed (they gained 50% of IGR), while the distance to stream, stream density, and elevation have an overwhelming effect in Al-Buaida watershed (they gained 44% of IGR). For flood susceptibility mapping, RF outperformed the other two algorithms for both watersheds and was thus selected for susceptibility mapping. The maps were classified into five classes, and 11% of Al-Buaida watershed fell into high to very high classes, while 5.2% of Zarqa Ma'in watershed fell within these classes. In conclusion, MLAs were able to produce susceptibility maps efficiently, and they can form an alternative to physical modeling.

关键词： machine learning algorithms flood susceptibility random forest artificial neural networks support vector machine

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Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and machine learning algorithms

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷

作者： Liu, Jie Feng, Qisheng Liang, Tiangang Yin, Jianpeng Gao, Jinlong Ge, Jing Hou, Mengjing Wu, Caixia Li, Wenlong Lanzhou Univ State Key Lab Grassland Agroecosyst Lanzhou 730000 Peoples R China Lanzhou Univ Key Lab Grassland Livestock Ind Innovat Minist Agr & Rural Affairs Lanzhou 730000 Peoples R China Lanzhou Univ Engn Res Ctr Grassland Ind Minist Educ Lanzhou 730000 Peoples R China Lanzhou Univ Coll Pastoral Agr Sci & Technol Lanzhou 730000 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Sichuan Peoples R China

The neutral detergent fiber (NDF) content is a key factor in the forage palatability and livestock digestibility of alpine grassland. Traditional methods of measuring NDF are time-consuming and laborious, while multispectral remote sensing often has difficulty capturing subtle changes due to its broad spectral channels. In this study, we evaluate NDF using hyperspectral data and environmental factors (EFs) during 2016-2019. The artificial neural network (ANN), support vector machine (SVM), and random forest (RF) are used to construct models. The results show that: 1) compared with the correlation analysis, the least absolute shrinkage and selection operator (LASSO) regression can more effectively select the important variables for estimating NDF and achieve significant dimension reduction;2) estimation accuracy of the models (coefficient of determination (R-2) between 0.44 and 0.51, relative root mean square error (RMSE) between 4.31% and 4.61%) based on first derivative (FD) spectra is relatively better than the models based on original spectra (OR), log transformation (Log), and continuum removal (CR) spectra (R-2 between 0.35 and 0.46, RMSE between 4.39% and 4.92%). The RF model based on FD-Log-vegetation indices (VIs)-EFs is the best model (R-2 = 0.62, RMSE = 3.98%);and 3) compared with the EFs, hyperspectral feature plays a pivotal role in estimating NDF, with a total contribution of 81.66%, and red-edge and shortwave infrared (SWIR) regions are significant for estimating NDF. In general, the combination of hyperspectral data with the EFs significantly improves the accuracy of estimating NDF. This study provides important guidance for future efforts on estimating NDF of natural grassland using remote sensing and machine learning algorithms.

关键词： Biological system modeling Monitoring Hyperspectral imaging Radio frequency Support vector machines machine learning algorithms Indexes Alpine grassland hyperspectral remote sensing machine learning neutral detergent fiber (NDF)

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Using machine learning algorithms to Determine the Post-COVID State of a Person by Their Rhythmogram

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SENSORS 2023年第11期23卷 5272页

作者： Stasenko, Sergey V. Kovalchuk, Andrey V. Eremin, Evgeny V. Drugova, Olga V. Zarechnova, Natalya V. Tsirkova, Maria M. Permyakov, Sergey A. Parin, Sergey B. Polevaya, Sofia A. Lobachevsky State Univ Nizhny Novgorod Inst Biol & Biomed Neurotechnol Dept Nizhnii Novgorod 603022 Russia Russian Acad Sci Inst Appl Phys Lab Autowave Proc Nizhnii Novgorod 603950 Russia Lobachevsky State Univ Nizhny Novgorod Fac Social Sci Nizhnii Novgorod 603022 Russia Privolzhsky Res Med Univ Dept Med Biophys Nizhnii Novgorod 603005 Russia GBUZ NO Nizhny Novgorod Reg Clin Oncol Dispensary Nizhnii Novgorod 603126 Russia Privolzhsky Dist Med Ctr Clin Hosp 2 Nizhnii Novgorod 603032 Russia

This study introduces a novel method for detecting the post-COVID state using ECG data. By leveraging a convolutional neural network, we identify "cardiospikes" present in the ECG data of individuals who have experienced a COVID-19 infection. With a test sample, we achieve an 87 percent accuracy in detecting these cardiospikes. Importantly, our research demonstrates that these observed cardiospikes are not artifacts of hardware-software signal distortions, but rather possess an inherent nature, indicating their potential as markers for COVID-specific modes of heart rhythm regulation. Additionally, we conduct blood parameter measurements on recovered COVID-19 patients and construct corresponding profiles. These findings contribute to the field of remote screening using mobile devices and heart rate telemetry for diagnosing and monitoring COVID-19.

关键词： machine learning algorithms electrocardiogram post-COVID state COVID-19 data analysis

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Implementing machine learning algorithms to Predict Particulate Matter (PM2.5): A Case Study in the Paso del Norte Region

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ATMOSPHERE 2022年第12期13卷 2100-2100页

作者： Mahmud, Suhail Ridi, Tasannum Binte Islam Miah, Mohammad Sujan Sarower, Farhana Elahee, Sanjida Karen Clark & Co Boston MA 02116 USA East West Univ Dept Econ Dhaka 1212 Bangladesh Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA New Mexico Sate Univ Math Dept Las Cruces NM 88003 USA Penn State Univ Dept Chem State Coll PA 16801 USA

This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality's monitoring stations corresponding to 2014-2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models.

关键词： ground PM air quality machine learning algorithms Paso del Norte classification

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