Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment(WEEE)in developing countries,this article focuses on predicting the WEEE generated by Cameroonian small and medi...Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment(WEEE)in developing countries,this article focuses on predicting the WEEE generated by Cameroonian small and medium enterprises(SMEs)that are engaged in ISO 14001:2015 initiatives and consume electrical and electronic equipment(EEE)to enhance their performance and profitability.The methodology employed an exploratory approach involving the application of general equilibrium theory(GET)to contextualize the study and generate relevant parameters for deploying the random forest regression learning algorithm for predictions.Machine learning was applied to 80%of the samples for training,while simulation was conducted on the remaining 20%of samples based on quantities of EEE utilized over a specific period,utilization rates,repair rates,and average lifespans.The results demonstrate that the model’s predicted values are significantly close to the actual quantities of generated WEEE,and the model’s performance was evaluated using the mean squared error(MSE)and yielding satisfactory results.Based on this model,both companies and stakeholders can set realistic objectives for managing companies’WEEE,fostering sustainable socio-environmental practices.展开更多
Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection ...Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.展开更多
BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intr...BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.展开更多
Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obs...Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.展开更多
Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature revie...Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature review and expert discussion.Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively analyzed.Based on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was analyzed.Results:We obtained 4806 analyzable data from 1625 pregnant women.Among these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set F2.Each of F1 and F2 was used for training the random forest algorithm.The overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 model.To explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression algorithm.The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy.In addition,there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.展开更多
Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling an...Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.展开更多
目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析...目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析,构建全身麻醉复苏延迟的预判模型并用列线图展示。利用随机森林算法筛选全身麻醉患者复苏延迟的影响因素并按重要性排序。采用受试者操作特征曲线(Receiver operating characteristic curve,ROC)下面积(Area of the under curve,AUC)检验模型的预测效果,采用校准曲线以及决策曲线综合评价模型的预测性能。结果1177例患者复苏延迟发生99例,发生率为8.41%。Logistic回归显示性别、ASA分级、年龄、手术时间、手术种类、输液量是全麻患者复苏延迟的独立危险因素。随机森林算法结果显示复苏延迟各变量的重要性排序为手术种类、年龄、手术时间、输液量、ASA分级、性别。Logistic回归模型的训练组AUC为0.87(95%CI 0.83~0.91),验证组为0.86(95%CI 0.81~0.91)。随机森林模型训练组AUC为0.85(95%CI 0.49~1.00),验证组AUC为0.76(95%CI 0.26~1.00)。提示模型具有良好的区分能力,预测能力较高,具有一定的临床价值。结论手术种类、年龄、手术时间、输液量、ASA分级、性别是全麻患者复苏延迟的独立危险因素,根据此构建预判模型的区分度与校准度较高,有助于预测全麻患者苏醒延迟的发生,可以为临床护理干预措施的制定与实施提供参考。展开更多
文摘Given the challenge of estimating or calculating quantities of waste electrical and electronic equipment(WEEE)in developing countries,this article focuses on predicting the WEEE generated by Cameroonian small and medium enterprises(SMEs)that are engaged in ISO 14001:2015 initiatives and consume electrical and electronic equipment(EEE)to enhance their performance and profitability.The methodology employed an exploratory approach involving the application of general equilibrium theory(GET)to contextualize the study and generate relevant parameters for deploying the random forest regression learning algorithm for predictions.Machine learning was applied to 80%of the samples for training,while simulation was conducted on the remaining 20%of samples based on quantities of EEE utilized over a specific period,utilization rates,repair rates,and average lifespans.The results demonstrate that the model’s predicted values are significantly close to the actual quantities of generated WEEE,and the model’s performance was evaluated using the mean squared error(MSE)and yielding satisfactory results.Based on this model,both companies and stakeholders can set realistic objectives for managing companies’WEEE,fostering sustainable socio-environmental practices.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number(IF2-PSAU-2022/01/22043)。
文摘Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in children.Parents can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five years.This research study aims to develop an automated tool for diagnosing autism in children.The computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification phases.The most deterministic features are selected from the self-acquired dataset by novel feature selection methods before classification.The Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this study.The performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research work.The experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired dataset.The ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different classifiers.The Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.
基金the Chinese Clinical Trial Registry(No.ChiCTR2000040109)approved by the Hospital Ethics Committee(No.20210130017).
文摘BACKGROUND Difficulty of colonoscopy insertion(DCI)significantly affects colonoscopy effectiveness and serves as a key quality indicator.Predicting and evaluating DCI risk preoperatively is crucial for optimizing intraoperative strategies.AIM To evaluate the predictive performance of machine learning(ML)algorithms for DCI by comparing three modeling approaches,identify factors influencing DCI,and develop a preoperative prediction model using ML algorithms to enhance colonoscopy quality and efficiency.METHODS This cross-sectional study enrolled 712 patients who underwent colonoscopy at a tertiary hospital between June 2020 and May 2021.Demographic data,past medical history,medication use,and psychological status were collected.The endoscopist assessed DCI using the visual analogue scale.After univariate screening,predictive models were developed using multivariable logistic regression,least absolute shrinkage and selection operator(LASSO)regression,and random forest(RF)algorithms.Model performance was evaluated based on discrimination,calibration,and decision curve analysis(DCA),and results were visualized using nomograms.RESULTS A total of 712 patients(53.8%male;mean age 54.5 years±12.9 years)were included.Logistic regression analysis identified constipation[odds ratio(OR)=2.254,95%confidence interval(CI):1.289-3.931],abdominal circumference(AC)(77.5–91.9 cm,OR=1.895,95%CI:1.065-3.350;AC≥92 cm,OR=1.271,95%CI:0.730-2.188),and anxiety(OR=1.071,95%CI:1.044-1.100)as predictive factors for DCI,validated by LASSO and RF methods.Model performance revealed training/validation sensitivities of 0.826/0.925,0.924/0.868,and 1.000/0.981;specificities of 0.602/0.511,0.510/0.562,and 0.977/0.526;and corresponding area under the receiver operating characteristic curves(AUCs)of 0.780(0.737-0.823)/0.726(0.654-0.799),0.754(0.710-0.798)/0.723(0.656-0.791),and 1.000(1.000-1.000)/0.754(0.688-0.820),respectively.DCA indicated optimal net benefit within probability thresholds of 0-0.9 and 0.05-0.37.The RF model demonstrated superior diagnostic accuracy,reflected by perfect training sensitivity(1.000)and highest validation AUC(0.754),outperforming other methods in clinical applicability.CONCLUSION The RF-based model exhibited superior predictive accuracy for DCI compared to multivariable logistic and LASSO regression models.This approach supports individualized preoperative optimization,enhancing colonoscopy quality through targeted risk stratification.
文摘Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.
基金supported by the Qingdao Municipal Bureau of Science and Technology(No.19-6-1-55-nsh)。
文摘Objective:To study the application of a machine learning algorithm for predicting gestational diabetes mellitus(GDM)in early pregnancy.Methods:This study identified indicators related to GDM through a literature review and expert discussion.Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis,and the collected indicators were retrospectively analyzed.Based on Python,the indicators were classified and modeled using a random forest regression algorithm,and the performance of the prediction model was analyzed.Results:We obtained 4806 analyzable data from 1625 pregnant women.Among these,3265 samples with all 67 indicators were used to establish data set F1;4806 samples with 38 identical indicators were used to establish data set F2.Each of F1 and F2 was used for training the random forest algorithm.The overall predictive accuracy of the F1 model was 93.10%,area under the receiver operating characteristic curve(AUC)was 0.66,and the predictive accuracy of GDM-positive cases was 37.10%.The corresponding values for the F2 model were 88.70%,0.87,and 79.44%.The results thus showed that the F2 prediction model performed better than the F1 model.To explore the impact of sacrificial indicators on GDM prediction,the F3 data set was established using 3265 samples(F1)with 38 indicators(F2).After training,the overall predictive accuracy of the F3 model was 91.60%,AUC was 0.58,and the predictive accuracy of positive cases was 15.85%.Conclusions:In this study,a model for predicting GDM with several input variables(e.g.,physical examination,past history,personal history,family history,and laboratory indicators)was established using a random forest regression algorithm.The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy.In addition,there are cer tain requirements for the propor tions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.
文摘Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.
文摘目的基于Logistic回归和随机森林算法构建全身麻醉复苏延迟的预判模型并验证。方法选择2021—2023年浙江某三甲医院复苏室收治的1177例全麻患者作为研究对象,按7︰3的比例随机分为训练组和验证组两组,采用Logistic单因素+多因素回归分析,构建全身麻醉复苏延迟的预判模型并用列线图展示。利用随机森林算法筛选全身麻醉患者复苏延迟的影响因素并按重要性排序。采用受试者操作特征曲线(Receiver operating characteristic curve,ROC)下面积(Area of the under curve,AUC)检验模型的预测效果,采用校准曲线以及决策曲线综合评价模型的预测性能。结果1177例患者复苏延迟发生99例,发生率为8.41%。Logistic回归显示性别、ASA分级、年龄、手术时间、手术种类、输液量是全麻患者复苏延迟的独立危险因素。随机森林算法结果显示复苏延迟各变量的重要性排序为手术种类、年龄、手术时间、输液量、ASA分级、性别。Logistic回归模型的训练组AUC为0.87(95%CI 0.83~0.91),验证组为0.86(95%CI 0.81~0.91)。随机森林模型训练组AUC为0.85(95%CI 0.49~1.00),验证组AUC为0.76(95%CI 0.26~1.00)。提示模型具有良好的区分能力,预测能力较高,具有一定的临床价值。结论手术种类、年龄、手术时间、输液量、ASA分级、性别是全麻患者复苏延迟的独立危险因素,根据此构建预判模型的区分度与校准度较高,有助于预测全麻患者苏醒延迟的发生,可以为临床护理干预措施的制定与实施提供参考。
