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Automated Screening of Hip X-rays for Osteoporosis by Singh's Index Using machine learning algorithms

INDIAN JOURNAL OF ORTHOPAEDICS 2024年第10期58卷 1449-1457页

作者： Kalavakonda, Vijaya Mohamed, Sameer Abhay, Lal Muthu, Sathish SRM Inst Sci & Technol Fac Engn & Technol Sch Comp Dept Comp Technol Chennai India Sri Ramachandra Inst Higher Educ & Res Chennai India Orthopaed Res Grp Coimbatore Tamil Nadu India Karpagam Acad Higher Educ Fac Engn Dept Biotechnol Coimbatore Tamil Nadu India Govt Med Coll Dept Orthopaed Karur Tamil Nadu India

IntroductionOsteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning *** used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms-Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)-for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose *** ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score *** superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI.

关键词： Osteoporosis screening Singh's index machine learning algorithms Hip radiographs Convolutional neural networks

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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

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Energy Storage and Saving 2024年第4期3卷 243-249页

作者： Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor Petroleum Systems Engineering Faculty of Engineering and Applied ScienceUniversity of ReginaReginaSK S4S 0A2Canada Department of Chemical Engineering University of Mohaghegh ArdabiliArdabil 13131-56199Iran Department of Chemistry School of ScienceAlzahra UniversityTehran ***Iran Computer Engineering Gachsaran Oil and Gas Exploitation CompanyGachsaran ***Iran Architectural Engineering Shiraz Islamic Azad UniversityShiraz ***Iran

A Trombe wall-heating system is used to absorb solar energy to heat *** parameters affect the system performance for optimal *** study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall *** accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)*** results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and *** contrast,linear regression and decision tree showed weaker *** findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关键词： Trombe wall Solar energy Thermal storage wall machine learning algorithms

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Analysis of decarbonization path in New York state and forecasting carbon emissions using different machine learning algorithms

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CARBON NEUTRALITY 2024年第1期3卷 8页

作者： Ekaterina, Glukhova Li, Jia Shanghai Jiao Tong Univ China UK Low Carbon Coll 3 Yinlian Rd Shanghai 201306 Peoples R China

The state of New York admitted 143 million metric tons of carbon emissions from fossil fuels in 2020, prompting the ambitious goal set by the CLCPA to achieve carbon neutrality. The paper focused on analyzing and predicting carbon emissions using four different machine-learning algorithms. It examined emissions from fossil fuel combustion from 1990 to 2020 and validated four different algorithms to choose the most effective one for predicting emissions from 2020 to 2050. The analysis covered various economic sectors including transportation, residential, commercial, industrial, and electric power. By analyzing policies, the paper forecasted emissions for 2030 and 2050, leading to the identification of different pathways to reach carbon neutrality. The research concluded that in order to achieve neutrality, radical measures must be taken by the state of New York. Additionally, the paper compared the most recent data for 2021 with the forecasts, showing that significant measures need to be implemented to achieve the goal of carbon neutrality. Despite some studies assuming a trend of decreased emissions, the research revealed different results. The paper presents three pathways, two of which follow the ambitious plan to reach carbon neutrality. As a result, the emission amount by 2050 for the different pathways was projected to be 31.1, 22.4, and 111.95 of MMt CO2 e, showcasing the need for urgent action to combat climate change.

关键词： machine learning algorithms Forecasting Extreme learning machine Support Vector machine Hybrid models New York CO2 emission

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Integrating chemokines and machine learning algorithms for diagnosis and bleeding assessment in primary immune thrombocytopenia: A prospective cohort study

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BRITISH JOURNAL OF HAEMATOLOGY 2024年第5期205卷 1938-1950页

作者： Wen, Qing Sun, Ting Chen, Jia Li, Yang Liu, Xiaofan Li, Huiyuan Fu, Rongfeng Liu, Wei Xue, Feng Ju, Mankai Dong, Huan Dai, Xinyue Wang, Wentian Chi, Ying Yang, Renchi Chen, Yunfei Zhang, Lei Chinese Acad Med Sci & Peking Union Med Coll Inst Hematol & Blood Dis Hosp CAMS Key Lab Gene Therapy Blood Dis Haihe Lab Cell EcosystState Key Lab Expt Hematol Tianjin 300020 Peoples R China Tianjin Inst Hlth Sci Tianjin Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Sch Populat Med & Publ Hlth Beijing Peoples R China

Primary immune thrombocytopenia (ITP) is an autoimmune bleeding disorder, and chemokines have been shown to be dysregulated in autoimmune disorders. We conducted a prospective analysis to identify potential chemokines that could enhance the diagnostic accuracy and bleeding evaluation in ITP patients. In the discovery cohort, a Luminex-based assay was employed to quantify concentrations of plasma multiple chemokines. These levels were subjected to comparative analysis using a cohort of 60 ITP patients and 17 patients with thrombocytopenia other than ITP (non-ITP). Additionally, comparative evaluation was conducted between a subgroup of 12 ITP patients characterised by bleeding episodes (ITP-B, as defined by an ITP-2016 bleeding grade >= 2) and 33 ITP patients without bleeding episodes (ITP-NB, as defined by an ITP-2016 bleeding grade <= 1). machine learning algorithms further identified CCL20, interleukin-2, CCL26, CCL25, and CXCL1 as promising indicators for accurate diagnosis of ITP and CCL21, CXCL8, CXCL10, CCL8, CCL3, and CCL15 as biomarkers for assessing bleeding risk in ITP patients. The results were confirmed using enzyme-linked immunosorbent assays in a validation cohort (43 ITP patients and 19 non-ITP patients). Overall, the findings suggest that specific chemokines show promise as potential biomarkers for diagnosis and bleeding evaluation in ITP patients.

关键词： bleeding risk chemokine diagnosis ITP machine learning algorithms

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Power System Load Forecasting Using machine learning algorithms: Optimal Approach

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2024年第3期14卷 458-467页

作者： Babu, M. Ravindra Chintalapudi, V. Suresh Kalyan, Ch. Nagasai Bhaskar, K. Krishna Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Narasaraopet Dept Elect & Elect Engn Narasaraopet 522601 Andhra Pradesh India Vasireddy Venkatadri Inst Technol Dept Elect & Elect Engn Guntur 522508 Andhra Pradesh India Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Kakinada Dept Mech Engn Kakinada 533003 Andhra Pradesh India

Accurate forecasting of long-term electricity demand has a prominent role in demand side management and electricity system planning and operation. Demand over-estimation gives rise to over-investment in system assets, driving up the electric power prices, while demand under-estimation may give on to under-investment results in unreliable and insecure electricity. The electrical load on a station varies more dynamically and hence forecasting of load values is more significant. In long-term load forecasting (LF), economic factors, weather conditions, time factors and random effects plays a prominent role. machine learning (ML) algorithms often used to estimate the future values constructed on the inferences drawn from the existing values. For these algorithms, the input data is supplied for training and as well testing the algorithm efficacy. Now a days, large amount of data is available everywhere. Therefore, it is essential to analyze this data in order to extract some profitable information and to implement an algorithm based on this analysis. These algorithms works based on historical relationship between the various factors considered in the problem. In this paper, some popular ML algorithms such as Linear develop the Forecasting model for standard 14 and 30 bus systems. The efficiency of the forecasting procedure is examined based on RMSE, Accuracy and time taken to generate the model. Further, the optimization results encourages for implementing these algorithms for real time operations in order to minimize number of loss violation instances in a given system

关键词： Load Forecasting machine learning algorithms Support Vector machine KNN Regression

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

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JOURNAL OF HEALTH POPULATION AND NUTRITION 2024年第1期43卷 1-13页

作者： Reza, Tasneem Binte Salma, Nahid Jahangirnagar Univ Dept Stat & Data Sci Dhaka 1342 Bangladesh

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual's health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017-2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML *** pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector machine), NB (Na & iuml;ve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model's performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC *** shows, Boruta algorithm identifies eleven significant features including respondent's age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm's significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model's respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy =

关键词： Low birth weight Boruta algorithm Wrapper method Key features machine learning algorithms

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Determinants of carbon emissions in Africa: new evidence based on machine learning algorithms

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-34页

作者： Ayimadu, Edwin Twum Liu, Yaolin Asante, Isaac Osei Dunee, Donatus Mwakipunda, Grant Charles Wuhan Univ Sch Resource & Environm Sci 299 Luoyu Rd10 Wuchang Dist Wuhan Hubei Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Minist Educ Key Lab Aerosp Informat Secur & Trusted Comp Wuhan 430072 Peoples R China Wuhan Univ Res Inst Environm Law 299Bayi Rd Wuhan Hubei Peoples R China China Univ Geosci Key Lab Theory & Technol Petr Explorat & Dev Hubei Wuhan 430074 Peoples R China

Balancing economic expansion and environmental sustainability in Africa is critical, as rising CO2 emissions threaten the continent's commitment to global climate goals. Despite widespread acknowledgment, a significant gap in understanding the sector-specific contributions to emissions persists. This empirical study examines the impact of economic expansion, total natural resource rents, and energy consumption on CO2 emissions in 48 African countries from 2000 to 2022. The study utilized a combination of linear and nonlinear machine learning models, OLS-linear regression, random forest, extra trees, histogram gradient boosting, and decision trees to assess the intricate relationships between these variables. Ensemble methods, incorporating boosting and bagging techniques, were also employed to enhance model accuracy. Additionally, impulse response and variance decomposition analyses were conducted to forecast the long-term impact of variable changes on CO2 over the next decade. The findings reveal several critical insights: First, economic expansion and energy consumption promote CO2 emissions in the study area. The same hypothesis was repeated for their interactive effect on CO2. Second, total natural resource rents positively impact environmental sustainability across the continent. The analysis indicates that if these trends are left unchecked, they could severely undermine efforts to achieve environmental sustainability in Africa. Third, the impulse response and variance decomposition results indicate that over the next ten years, variations in energy use will have a more substantial impact on CO2 than fluctuations in natural resource rents and economic expansion. We, therefore, suggested a valuable baseline for economy-energy-resources-carbon nexus policy implications to reduce CO2 across the continent.

关键词： Environmental sustainability Emissions machine learning algorithms Energy consumption Variance decomposition

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Identification and verification of diagnostic biomarkers for deep infiltrating endometriosis based on machine learning algorithms

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JOURNAL OF BIOLOGICAL ENGINEERING 2024年第1期18卷 1-17页

作者： Shi, Shanping Huang, Chao Tang, Xiaojian Liu, Hua Feng, Weiwei Chen, Chen Shanghai Jiao Tong Univ Ruijin Hosp Sch Med Dept Obstet & Gynecol Shanghai 200025 Peoples R China

This study addresses the challenges in the early diagnosis of deep infiltrating endometriosis (DIE) by exploring the potential role of the deubiquitinating enzyme USP14. By analyzing the GSE141549 dataset from the Gene Expression Omnibus (GEO) database, using bioinformatics methods and three machine learning algorithms (LASSO, Random Forest, and Support Vector machine), the key feature gene USP14 was identified. The results indicated that USP14 is significantly upregulated in DIE and exhibits good predictive value (AUC = 0.786). Further analysis revealed the important role of USP14 in muscle function, cellular growth factor response, and maintenance of chromosome structure, and its close association with various immune cell functions. Immunohistochemical staining confirmed the high expression of USP14 in DIE tissues. This study provides a new molecular target for the early diagnosis of DIE, which holds significant clinical implications and potential application value.

关键词： Deep infiltrating endometriosis (DIE) USP14 machine learning algorithms Single-cell RNA sequencing (scRNA-seq) Immunohistochemical staining

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Prediction of air compressor condition using vibration signals and machine learning algorithms

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JOURNAL OF VIBRATION AND CONTROL 2023年第5-6期29卷 1342-1351页

作者： Aravinth, S. Sugumaran, V Vellore Inst Technol Sch Mech Engn Chennai CampusVandalur Kelambakkam Rd Chennai 600127 Tamil Nadu India

Air compressors are widely used equipment in the modern world for their tremendous utilization in applications of both domestic and industrial sectors. The inbuilt mechanical parts are often prone to various failures due to the complexity in the construction of air compressors that affects the overall system process. Hence, it is essential to devise a methodology to identify the failures at the early stages of its operation to avoid the major causalities due to process breakdown and system seizure. In this study, a single-acting single-stage reciprocating air compressor was chosen. The fault conditions like inlet valve fluttering, outlet valve fluttering, valve plate leakage, and check valve fault were considered. The statistical, histogram and autoregressive moving average features were extracted from the raw vibration signals. The most dominating features were selected using a decision tree algorithm and those features were classified using machine learning classifiers like Lazy K Star, Decorate, and radial basis function networks. The classifier Lazy K Star on autoregressive moving average feature exhibits the highest fault classification rate of 99.67% in classifying various compressor conditions and the results were compared and presented.

关键词： Air compressor condition monitoring fault diagnosis machine learning algorithms vibration signals

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Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms

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International Journal of Digital Earth 2023年第1期16卷 3706-3724页

作者： Hengxing Xiang Yanbiao Xi Dehua Mao Tianyuan Xu Ming Wang Fudong Yu Kaidong Feng Zongming Wang State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunPeople’s Republic of China International Institute for Earth System Science Nanjing UniversityNanjingPeople’s Republic of China School of Foreign Languages Zhongnan University of Economics and LawWuhanPeople’s Republic of China College of Information and Technology Jilin Agricultural UniversityChangchunPeople’s Republic of China National Earth System Science Data Center BeijingPeople’s Republic of China

Climate change and human activities have reduced the area and degraded the functions and services of wetlands in *** protect and restore wetlands,it is urgent to predict the spatial distribution of potential *** this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning *** on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning *** accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of *** area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast *** features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.

关键词： Potential wetland distribution machine learning algorithms geographic big data China wetland geographic features

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