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Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms

Emerging Trends in Drugs, Addictions, and Health 2025年 5卷 100165-100165页

作者： Chandrashekarappa, Shivashankar Basapura Assi, Sulaf Jayabalan, Manoj Al-Hamid, Abdullah Al-Jumeily, Dhiya Oracle India Pvt. Ltd. Bangalore India Computer Sciences and Mathematics Liverpool John Moores University Liverpool United Kingdom Pharmacy and Biomolecular Sciences Liverpool John Moores University Liverpool United Kingdom Department of Pharmacy Practice College of Clinical Pharmacy King Faisal University Al-Ahsa Saudi Arabia

Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic diseases. Several factors play a role in the increased prescription of opioids, relating to patients’ lifestyle, characteristics, and disease. As these factors are complex in nature, understanding them requires machine learning approach. This study explored overprescribing opioids among nephrologists in the US using unsupervised machine learning algorithms. Design: Two types of unsupervised clustering were applied to the Medicare Provider Utilisation and Payment Data Part-D Prescriber Summary. Setting: The dataset had 50,134 records with 85 features relating to opioids prescription per US state. Univariate and bivariate analysis were applied first to gain understanding of the data followed by K-mean clustering and Gaussian Mixture Models. Findings: Unsupervised clustering showed that prescription issued to males were three times higher than those issued to females. Moreover, male nephrologists were higher prescribers than female nephrologists, and a third of male nephrologists were high prescribers of opioids. The highest rates of prescriptions were seen in California. Conclusions: Unsupervised machine learning algorithms enabled understanding of high opioid prescription across gender and US state by analysing multiple features. Both K-mean clustering and Gaussian Mixture Models achieved the same outcomes. Future work will benefit from applying deep learning in order to understand in-depth patterns in prescription and contributing factors related to over-prescribing. © 2024 The Authors

关键词： Epidemic Gaussian mixture models K-mean clustering machine learning algorithms Nephrologists Opioids

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Prediction of the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids using different machine learning algorithms

Case Studies in Chemical and Environmental Engineering

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Graish, Mohammed Shorbaz Ali, Ali B.M. Al-Zahiwat, Murtadha M. Alardhi, Saja Mohsen Baghoolizadeh, Mohammadreza Salahshour, Soheil Pirmoradian, Mostafa Department of Chemical Engineering University of Technology- Iraq Baghdad 10066 Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Chemical Engineering College of Engineering University of Misan Amarah Iraq Nanotechnology and Advanced Materials Research Center University of Technology – Iraq Baghdad Iraq Department of Mechanical Engineering Shahrekord University Shahrekord 88186-34141 Iran Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Department of Mechanical Engineering Khomeinishahr branch Islamic Azad University Khomeinishahr Iran

Viscosity is a crucial parameter for heat transfer systems, governing pumping power, Rayleigh number, and Reynolds number;thus, viscosity prediction for hybrid nanofluids is important. Although some studies have employed ML algorithms for predicting viscosity, limited ML algorithms or specific nanofluid types were examined in previous studies, disregarding the complexities involved in the rheological behavior of a complex nanofluid system such as non-Newtonian hybrid nanofluids. To overcome this limitation, this study offers a practical contribution by utilizing 20 different machine-learning models to predict the viscosity of iron-CuO/water-ethylene glycol non-Newtonian hybrid nanofluids. The influences of the input variables: solid volume fraction (SVF), temperature, and shear rate on viscosity prediction are systematically assessed. We evaluate the prediction accuracy and reliability of algorithms using ten performance metrics including RMSE, MAE, R² and NSE. Multivariate Polynomial Regression (MPR) outperforms the other algorithms, which is evident in the highest correlation coefficient (R² = 0.992) and lowest error metrics. At the other end, is the Extreme learning machine (ELM), which turns out to be the worst performer. A unique contribution of this paper is that we extract a mathematical equation from the MPR model that allows for straightforward calculation of viscosity, avoiding non-trivial ML computations. This simplicity aids in practical applications and increases usefulness for engineers and researchers alike. Using advanced data visualization techniques (heatmaps, box plots, KDE plots and Taylor diagrams), the relationships between input variables and viscosity as well as the model performance are explored. These results give a better understanding of the non-Newtonian hybrid nanofluid behavior and a solid predictor of design-efficient heat transfer systems. © 2025 The Authors

关键词： machine learning algorithms Non-Newtonian hybrid nano- antifreeze Viscosity

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Applications of machine learning algorithms on the compressive strength of laterite blocks made with metakaolin-based geopolymer and sugarcane molasses

Waste Management Bulletin

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Waste Management Bulletin 2025年第3期3卷

作者： Sinkhonde, David Mirindi, Derrick Dabakuyo, Ismael Bezabih, Tajebe Mashava, Destine Mirindi, Frederic Department of Civil and Construction Engineering Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Mechanical Engineering Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Economics African Economic Research Consortium (AERC) Nairobi Kenya

To refine the process of anticipating the structural integrity of laterite block components, the use of machine learning (ML) algorithms is required. This study initiates an exploration into forecasting the compressive strength of laterite blocks infused with metakaolin-based geopolymer (MKG) and sugarcane molasses (SM), utilizing machine learning techniques such as artificial neural networks (ANN), random forests (RF), decision trees (DT), and support vector machines (SVM). The models were developed using four input values, including the MKG, SM, laterite soil, and water, with compressive strength as the output. Results show that for all the models, the majority of the data points lie within the error lines range of −20 % and +20 %. Using the Taylor diagram model, the results demonstrate that the SVM (train) model achieves the highest performance in predicting the compressive strength of laterite blocks, with a correlation coefficient of 0.99 and the lowest root mean square error (RMSE) of 0.139. The correlation coefficient values (R) for training and testing algorithm models ranged between 0.65 and 0.99, implying that all models fairly predict the compressive strength of laterite blocks containing MKG and SM. The RF model emerges as an important model for generalization across training and testing phases, with R values of 0.9828 and 0.789, respectively. SHapley Additive exPlanations (SHAP) analysis assesses the model's explainability behavior. According to a SHAP-based feature importance study, age (85.33 %) and water content (17.87 %) are critical components that may improve compressive strength compared to MKG (8.60 %) and SM (6.74 %), respectively. This study not only assists in comprehending the essential parameters necessary for making well-informed decisions but also opens exciting possibilities for the application of ML in fostering sustainable construction practices. © 2025 The Authors

关键词： Laterite blocks machine learning algorithms Metakaolin-based geopolymer Sugarcane molasses Sustainable construction

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Flood Inundation Mapping of a River Stretch Using machine learning algorithms in the Google Earth Engine Environment

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Journal of Flood Risk Management 2025年第2期18卷

作者： Ashhar, Maaz Keesara, Venkata Reddy Sridhar, Venkataramana Department of Civil Engineering National Institute of Technology Warangal India Department of Biological Systems Engineering Virginia Polytechnic Institute and State University Blacksburg VA United States

Floods are among the most common natural disasters in India, causing significant socio-economic and environmental impacts. This study focuses on a frequently flooded stretch of the Godavari River in Telangana, India, to analyze the flood event that occurred between 14th July 2022 and 20th July 2022. Sentinel-1 SAR data from 6th July 2022 to 20th July 2022 were used to perform flood inundation mapping. Various machine learning algorithms, including Support Vector machine (SVM), Random Forest (RF), Gradient Boosting Tree (GBT), and Classification and Regression Tree (CART), were employed. The analysis revealed that out of the total study area of 1,556,544 ha, SVM classified 59,823 ha, RF classified 60,088 ha, GBT classified 57,497 ha, and CART classified 58,374 ha as flooded areas. In contrast, Otsu's Thresholding technique identified a significantly larger flooded area of 359,253 ha. For validation, 70 flooded and 30 non-flooded points were randomly selected from the flood map provided by the National Remote Sensing Center (NRSC). The RF algorithm achieved the best performance, correctly classifying 58 flooded points and 26 non-flooded points, resulting in an overall accuracy of 84%. The findings highlight the effectiveness of machine learning algorithms, particularly Random Forest, in flood inundation mapping. © 2025 The Author(s). Journal of Flood Risk Management published by Chartered Institution of Water and Environmental Management and John Wiley & Sons Ltd.

关键词： flood inundation map Google earth engine machine learning algorithms SAR data

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Assessing land cover change detection in a developing country with machine and deep learning algorithms: a case study

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Environment, Development and Sustainability 2025年 1-21页

作者： Hmamou, Mohamed Chafiq, Tarik Ouhammou, Imrane Lmoudden, Othmane Ait Raji, Mohammed Ouaskit, Said LTIM Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco LCMP Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco LGAGE Faculty of Sciences Ben M’Sik University Hassan II of Casablanca Casablanca B.P. 7955 Morocco

This study examines the evolution of land cover in the Fez province of Morocco through a detailed analysis of Land Cover Change Detection (LCCD). Using satellite imagery from Landsat-8 and Landsat-9, we generated high-resolution land cover maps by applying a range of machine learning and deep learning techniques. The analysis spans the period from 2014 to 2022, aiming to identify the most suitable algorithm for accurate LCCD. The methods tested in this research include Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Upon thorough comparison, SVM emerged as the top-performing model, yielding the most reliable classification results, while MLP demonstrated comparatively limited accuracy in this context. Our findings also underscore significant land use transitions, particularly an expansion of urban and industrial zones accompanied by a decline in agricultural lands trends largely attributed to anthropogenic pressures. These patterns are intricately linked to broader environmental shifts, including those related to climate. By analyzing spatial and temporal variations in land cover, this research contributes to a better grasp of the environmental transformations occurring in Fez, offering valuable knowledge to support evidence-based planning and the promotion of sustainable land use strategies. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Deep learning models GIS-based analysis Land cover classification machine learning algorithms Satellite image processing

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machine learning algorithms for Forecasting and Categorizing Euro-to-Dollar Exchange Rates

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IEEE ACCESS 2024年 12卷 74211-74217页

作者： El Mahjouby, Mohamed Bennani, Mohamed Taj Lamrini, Mohamed Bossoufi, Badre Alghamdi, Thamer A. H. El Far, Mohamed Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz Dept Comp Sci Lab LPAIS Fes 30003 Morocco Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar El Mahraz LIMAS Lab Fes 30003 Morocco Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff CF24 3AA Wales Al Baha Univ Sch Engn Elect Engn Dept Al Bahah 65779 Saudi Arabia

Forecasting changes in foreign exchange rates is a well-explored and widely recognized area within finance. Numerous research endeavors have delved into the utilization of methods in machine learning to analyze and predict movements in the foreign exchange market. This work employed several machine-learning techniques such as Adaboost, logistic regression, gradient boosting, random forest classifier, bagging, Gaussian na & iuml;ve Bayes, extreme gradient boosting classifier, decision tree classifier, and our approach (we have combined three models: logistic regression, random forest classifier, and Gaussian naive Bayes). Our objective is to predict the most advantageous times for purchasing and selling the euro about the dollar. We integrated a range of technical indicators into the training dataset to enhance the precision of our techniques and strategy. The outcomes of our experiment demonstrate that our approach outperforms alternative methods, achieving superior prediction performance. Our methodology yielded an accuracy of 0.948. This study will empower investors to make informed decisions about their future EUR/USD transactions, helping them identify the most advantageous times to buy and sell within the market.

关键词： machine learning Forecasting Currencies Classification algorithms Training Random forests machine learning algorithms Finance Logistic regression Foreign exchange prediction logistic regression random forest Na & iuml ve Bayes

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machine learning algorithms in Photovoltaics: Evaluating Accuracy and Computational Cost Across Datasets of Different Generations, Sizes, and Complexities

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JOURNAL OF ELECTRONIC MATERIALS 2024年第3期53卷 1517-1529页

作者： Al-Saban, Omar Alkadi, Muath Qaid, Saif M. H. Ahmed, Abdullah Ahmed Ali Abdellatif, Sameh O. British Univ Egypt BUE FabLab Ctr Emerging Learning Technol CELT Renewable Energy Postgrad Programme Cairo 11837 Egypt King Abdulaziz City Sci & Technol Riyadh Saudi Arabia King Saud Univ Coll Sci Dept Phys & Astron POB 2455 Riyadh 11451 Saudi Arabia Univ Hamburg Ctr Hybrid Nanostruct CHyN Hamburg Germany Univ Hamburg Fachbereich Phys D-20146 Hamburg Germany British Univ Egypt BUE Fac Engn FabLab Ctr Emerging Learning Technol CELT Elect Engn Dept Cairo 11837 Egypt

This paper evaluates the compatibility of five different machine learning (ML) algorithms for analyzing datasets extracted from solar cell devices. The selected ML algorithms span a range of complexity, from linear regression and polynomial regression models to random forest (RF), neural network, and a hybrid version of the latter two. The algorithms underwent testing with and without hyperparameter optimization to tune the model parameters. The study investigated the expected increased computational cost resulting from hyperparameter optimization, with improved ML accuracy. Additionally, five distinct datasets were utilized to measure the performance of the ML models across different dataset characteristics, including dataset size, features, and behavior. Dataset features are defined as the number of inputs and corresponding outputs, while dataset behavior captures the nature of the solar device. Various photovoltaic (PV) technologies, including conventional silicon p-n-junction cells, dye-sensitized solar cells (DSSC), perovskite solar cells (PSC), and perovskite-on-silicon tandem structures, were employed. The results indicate the superiority of the RF algorithm, demonstrating ultralow root-mean-square error, approaching 9.21E-07, across the datasets evaluated. Furthermore, the study investigates the substantial impact of hyperparameter optimization on neural network performance, demonstrating a remarkable increase in accuracy across diverse datasets. Notably, the influence is contingent upon the number of model parameters, with the neural network, which possesses the greatest number of parameters, showing the most significant improvement.

关键词： machine learning algorithms photovoltaic analysis dataset characteristics random forest hyperparameter optimization computational cost

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machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation

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ADVANCES IN SPACE RESEARCH 2022年第4期69卷 1799-1812页

作者： Chaudhary, Sumit Kumar Srivastava, Prashant K. Gupta, Dileep Kumar Kumar, Pradeep Prasad, Rajendra Pandey, Dharmendra Kumar Das, Anup Kumar Gupta, Manika Banaras Hindu Univ Inst Environm & Sustainable Dev Remote Sensing Lab Varanasi 221005 Uttar Pradesh India Banaras Hindu Univ Dept Phys Varanasi 221005 Uttar Pradesh India IIT BHU Dept Phys Varanasi 221005 Uttar Pradesh India Space Applicat Ctr ISRO Microwave Tech Dev Div MTDD Ahmadabad 380015 Gujarat India Univ Delhi Dept Geol New Delhi India

The present study provided the first-time comprehensive evaluation of 12 advanced statistical and machine learning (ML) algorithms for the Soil Moisture (SM) estimation from dual polarimetric Sentinel-1 radar backscatter. The ML algorithms namely support vector machine (SVM) with linear, polynomial, radial and sigmoid kernel, random forest (RF), multi-layer perceptron (MLP), radial basis function (RBF), Wang and Mendel's (WM), subtractive clustering (SBC), adaptive neuro fuzzy inference system (ANFIS), hybrid fuzzy interference system (HyFIS), and dynamic evolving neural fuzzy inference system (DENFIS) were used. Extensive field samplings were performed for collection of in-situ SM data and other parameters from the selected sites for seven different dates and at two different locations (Varanasi and Guntur District, India), concurrent to Sentinel-1 overpasses. The backscattering coefficients were considered as input variables and SM as output variable for the training, validation and testing of the ML algorithms. The site at Varanasi was used for the training, validation and testing of the models. On the other hand, the Guntur site was used as an independent site for checking the model performance, before finalizing the algorithms. The performances of different trained algorithms were evaluated in terms of correlation coefficient (r), root mean square error (RMSE) (in m(3)/m(3)) and bias (in m(3)/m(3)). The study identified the RF, SBC and ANFIS as the top three best performing models with comparable and promising SM estimation. In order to test the robustness of these best models (RF, SBC and ANFIS), further performance analysis was performed to the independent datasets of the Varanasi and Guntur test sites, which indicates that the performance of these three models were consistent and SBC can be recommended as the best among all for SM estimation. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Sentinel-1 Artificial Intelligence machine learning algorithms Soil Moisture Optimization

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machine learning algorithms for Satellite Image Classification Using Google Earth Engine and Landsat Satellite Data: Morocco Case Study

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IEEE ACCESS 2023年 11卷 71127-71142页

作者： Ouchra, Hafsa Belangour, Abdessamad Erraissi, Allae Hassan II Univ Fac Sci Ben MSik Lab Informat Technol & Modeling LTIM Casablanca 28830 Morocco Chouaib Doukkali Univ Polydisciplinary Fac Sidi Bennour El Jadida 24000 Morocco

Earth observation data have proven to be a valuable resource of quantitative information that is more consistent in time and space than traditional land-based surveys. Remote sensing plays a vital role in collecting data in many aspects of life, whether scientific, economic, or political. Land cover information is very important in supporting urban planning and decision-making and provides many opportunities for mapping and monitoring urban areas. Multiple data sources exist, including satellite data of different resolutions ranging from very high to medium resolution and aerial and drone image acquisitions. Today, accurate land cover information is in high demand. Using satellite imagery and remote sensing techniques for planning and development is becoming a common study conducted by many researchers to find practical solutions to the many problems affecting our planet. The recovery, management, and analysis of these large amounts of satellite imagery pose considerable challenges. The classification of satellite images is a very popular and complex topic. In classification studies over the last decade, researchers have been frequently studying only those three machine learning algorithms RF, CART, and SVM, applied in cities or countries except Morocco which poses a great lack of information on the land use of Morocco. To solve these challenges, six machine learning algorithms were applied and compared to each other based on several evaluation metrics, and then, to avoid the problems of data download and storage space, we used Google Earth Engine, a geospatial processing platform that operates in the cloud. It provides free access to substantial satellite data and free computations to monitor, visualize, and analyze environmental features at the petabyte scale. In this paper, we used Landsat 8 satellite data to perform a land cover classification of Morocco, applying machine learning algorithms, which is a subfield of artificial intelligence. This paper proposes an

关键词： & nbsp Remote sensing satellite image supervised classification Google earth engine LANDSAT satellite machine learning algorithms

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machine learning algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

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IEEE ACCESS 2020年 8卷 102075-102092页

作者： Noor, Nor Safira Elaina Mohd Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Malaysia Univ Sains Malaysia Sch Med Sci Dept Neurosci Hlth Campus Kota Baharu 16150 Kelantan Malaysia

Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI.

关键词： Electroencephalography machine learning algorithms prediction methods reviews

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