A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations

It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations.

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Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

Journal of Child Neurology ◽

10.1177/0883073818822358 ◽

2019 ◽

Vol 34 (4) ◽

pp. 221-229 ◽

Cited By ~ 6

Author(s):

Carlo M. Bertoncelli ◽

Paola Altamura ◽

Edgar Ramos Vieira ◽

Domenico Bertoncelli ◽

Susanne Thummler ◽

...

Keyword(s):

Machine Learning ◽

Cerebral Palsy ◽

Intellectual Disability ◽

Prediction Model ◽

Motor Skills ◽

Adaptive Functioning ◽

Learning Model ◽

Intelligence Scale ◽

Factors Associated ◽

Machine Learning Model

Background: Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy. Methods: This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as “mild,” “moderate,” “severe,” or “profound” based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher’s exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement” were followed. Results: Poor manual abilities ( P ≤ .001), gross motor function ( P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%. Conclusion: Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

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An Intelligent Rockburst Prediction Model Based on Scorecard Methodology

Minerals ◽

10.3390/min11111294 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1294

Author(s):

Honglei Wang ◽

Zhenlei Li ◽

Dazhao Song ◽

Xueqiu He ◽

Aleksei Sobolev ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

False Alarm ◽

False Alarm Rate ◽

Learning Model ◽

Underground Engineering ◽

Risk Levels ◽

Model Based ◽

Machine Learning Model ◽

Sample Category

Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were constructed using ChiMerge, evidence weight theory, and the logistic regression algorithm. An intelligent rockburst prediction model based on scorecard methodology (IRPSC) was obtained by integrating the three scorecards. The effects of hazard sample category weights on the missed alarm rate, false alarm rate, and accuracy of the IRPSC were analyzed. Results show that the accuracy, false alarm rate, and missed alarm rate of the IRPSC for rockburst prediction in riverside hydropower stations are 75%, 12.5%, and 12.5%, respectively. Setting higher hazard sample category weights can reduce the missed alarm rate of IRPSC, but it will lead to a higher false alarm rate. The IRPSC can adaptively adjust the threshold and weight value of the indicator and convert the abstract machine learning model into a tabular form, which overcomes the commonly black box problems of machine learning model, as well as is of great significance to the application of machine learning in rockburst risk prediction.

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Development of Prediction Model to Predict the Compressive Strength of Eco-Friendly Concrete Using Multivariate Polynomial Regression Combined with Stepwise Method

Materials ◽

10.3390/ma15010317 ◽

2022 ◽

Vol 15 (1) ◽

pp. 317

Author(s):

Hamza Imran ◽

Nadia Moneem Al-Abdaly ◽

Mohammed Hammodi Shamsa ◽

Amjed Shatnawi ◽

Majed Ibrahim ◽

...

Keyword(s):

Machine Learning ◽

Compressive Strength ◽

Prediction Model ◽

Polynomial Regression ◽

Learning Model ◽

Recycled Aggregate Concrete ◽

The Other ◽

Multivariate Polynomial ◽

Concrete Mixtures ◽

Machine Learning Model

Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.

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Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model

BMC Emergency Medicine ◽

10.1186/s12873-021-00447-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lingxiao He ◽

Lei Luo ◽

Xiaoling Hou ◽

Dengbin Liao ◽

Ran Liu ◽

...

Keyword(s):

Machine Learning ◽

Venous Thromboembolism ◽

Prediction Model ◽

Learning Model ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Screening Methods ◽

Trauma Patients ◽

Machine Learning Model ◽

Positive Rate

Abstract Background Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms. Methods We retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient’s electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported. Results The Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799). Conclusion The Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance.

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Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites

Scientific Reports ◽

10.1038/s41598-021-00218-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yingying Hu ◽

Ruijia Chen ◽

Haibing Gao ◽

Haitao Lin ◽

Jinye Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Spontaneous Bacterial Peritonitis ◽

Learning Model ◽

Apolipoprotein A1 ◽

Bacterial Peritonitis ◽

Reactive Protein ◽

Predictive Variables ◽

Machine Learning Model ◽

Cirrhotic Patients

AbstractSpontaneous bacterial peritonitis (SBP) is a life-threatening complication in patients with cirrhosis. We aimed to develop an explainable machine learning model to achieve the early prediction and outcome interpretation of SBP. We used CatBoost algorithm to construct MODEL-1 with 46 variables. After dimensionality reduction, we constructed MODEL-2. We calculated and compared the sensitivity and negative predictive value (NPV) of MODEL-1 and MODEL-2. Finally, we used the SHAP (SHapley Additive exPlanations) method to provide insights into the model’s outcome or prediction. MODEL-2 (AUROC: 0.822; 95% confidence interval [CI] 0.783–0.856), liked MODEL-1 (AUROC: 0.822; 95% CI 0.784–0.856), could well predict the risk of SBP in cirrhotic ascites patients. The 6 most influential predictive variables were total protein, C-reactive protein, prothrombin activity, cholinesterase, lymphocyte ratio and apolipoprotein A1. For binary classifier, the sensitivity and NPV of MODEL-1 were 0.894 and 0.885, respectively, while for MODEL-2 they were 0.927 and 0.904, respectively. We applied CatBoost algorithm to establish a practical and explainable prediction model for risk of SBP in cirrhotic patients with ascites. We also identified 6 important variables closely related to the occurrence of SBP.

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Machine Learning Model for Stock Market Prediction

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35822 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4057-4062

Author(s):

Dr. S. T. Patil

Keyword(s):

Machine Learning ◽

Stock Market ◽

Prediction Model ◽

Stock Price ◽

Stock Exchange ◽

Learning Model ◽

Stock Market Prediction ◽

Machine Learning Model

: In recent time’s stock market predictions is gaining more attention, maybe due to the fact that if the trend of the market is successfully predicted, the investors may be better guided. A stock exchange is a system where you can buy and sell stocks. By stock we mean the share in the ownership of the company. Companies buy stocks to get the money they need to grow. Whereas people buy the stocks, also called as securities as investment or ways of possibly earning money. A stock Market Prediction model will help people to predict particular company’s stock price before they want to invest. This system will help people to invest wisely.

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Early Prediction of Ventilator-associated Pneumonia in Critical Care Patients: a Machine Learning Model

10.21203/rs.3.rs-129953/v1 ◽

2020 ◽

Author(s):

Yingjian Liang ◽

Chengrui Zhu ◽

Cong Tian ◽

Qizhong Lin ◽

Zhiliang Li ◽

...

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Sensitivity And Specificity ◽

External Validation ◽

Model Performance ◽

Learning Model ◽

Ventilator Associated Pneumonia ◽

Data Set ◽

Machine Learning Model ◽

Mimic Iii

Abstract Background: This study was performed to develop and validate machine learning models for the early detection of ventilator-associated pneumonia (VAP) in patients 24 h before the diagnosis that enables VAP patients to receive early intervention and reduces the occurrence of complications.Patients and Methods: This study was based on the MIMIC-III dataset, which was a retrospective cohort. The random forest algorithm was applied to construct a base classifier, and the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the prediction model were evaluated. Meanwhile, a Clinical Pulmonary Infection Score (CPIS)-based model (threshold value≥3) using the same training and test data set was used as the control model.Results: A total of 38,515 ventilation durations occurred in 61,532 ICU admissions. VAP occurred in 212 of these durations. We incorporated 42 VAP risk factors on admission and routinely measured vital characteristics and laboratory results. Five-fold cross-validation was performed to evaluate the model performance, and the model achieved an AUC of 84.4%±1.7% on validation, 74.3%±2.5% sensitivity and 70.7.6%±1.2% specificity 24 h before the gold standard time (at least 48 h after ventilation). Our VAP machine learning model improved the AUC of the CPIS-based model by almost 25%, and the sensitivity and specificity were also improved by almost 14% and 15%, respectively.Conclusions: We developed and internally validated an automated model of VAP prediction in the MIMIC-III cohort. The VAP prediction model achieved high performance for AUC, sensitivity and specificity. and its performance was superior to that of the CPIS model. External validation and prospective interventional or outcome studies using this prediction model are envisioned as future work.

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Development of a Machine Learning Model for the Prediction of the Real Time Mortality in Patients in the Intensive Care Unit

10.21203/rs.3.rs-1066192/v1 ◽

2021 ◽

Author(s):

Jaeyoung Yang ◽

Hong-Gook Lim ◽

Wonhyeong Park ◽

Dongseok Kim ◽

Jin Sun Yoon ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Prediction Model ◽

Prediction Models ◽

External Validation ◽

Learning Model ◽

Mortality Prediction ◽

Machine Learning Model ◽

Mortality Prediction Model

Abstract BackgroundPrediction of mortality in intensive care units is very important. Thus, various mortality prediction models have been developed for this purpose. However, they do not accurately reflect the changing condition of the patient in real time. The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using four easy-to-collect vital signs.MethodsTwo independent retrospective observational cohorts were included in this study. The primary training cohort included the data of 1968 patients admitted to the intensive care unit at the Veterans Health Service Medical Center, Seoul, South Korea, from January 2018 to March 2019. The external validation cohort comprised the records of 409 patients admitted to the medical intensive care unit at Seoul National University Hospital, Seoul, South Korea, from January 2019 to December 2019. Datasets of four vital signs (heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation [SpO2]) measured every hour for 10 h were used for the development of the machine learning model. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset.ResultsThe machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. Thus, to investigate the importance of variables that influence the performance of the machine learning model, machine learning models were generated for each observation time or vital sign using the RF algorithm. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89). ConclusionsThe mortality prediction model developed in this study using data from only four types of commonly recorded vital signs is simpler than any existing mortality prediction model. This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.

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Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation

Diagnostics ◽

10.3390/diagnostics11030510 ◽

2021 ◽

Vol 11 (3) ◽

pp. 510

Author(s):

Sejong Oh ◽

Yuli Park ◽

Kyong Jin Cho ◽

Seong Jae Kim

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Diagnostic Performance ◽

Prediction Models ◽

Disease Diagnosis ◽

Learning Model ◽

Fundus Photography ◽

Support Vector ◽

Machine Learning Model ◽

Specific Prediction

The aim is to develop a machine learning prediction model for the diagnosis of glaucoma and an explanation system for a specific prediction. Clinical data of the patients based on a visual field test, a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, a general examination including an intraocular pressure (IOP) measurement, and fundus photography were provided for the feature selection process. Five selected features (variables) were used to develop a machine learning prediction model. The support vector machine, C5.0, random forest, and XGboost algorithms were tested for the prediction model. The performance of the prediction models was tested with 10-fold cross-validation. Statistical charts, such as gauge, radar, and Shapley Additive Explanations (SHAP), were used to explain the prediction case. All four models achieved similarly high diagnostic performance, with accuracy values ranging from 0.903 to 0.947. The XGboost model is the best model with an accuracy of 0.947, sensitivity of 0.941, specificity of 0.950, and AUC of 0.945. Three statistical charts were established to explain the prediction based on the characteristics of the XGboost model. Higher diagnostic performance was achieved with the XGboost model. These three statistical charts can help us understand why the machine learning model produces a specific prediction result. This may be the first attempt to apply “explainable artificial intelligence” to eye disease diagnosis.

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