scholarly journals Exploration of physiological sensors, features, and machine learning models for pain intensity estimation

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254108
Author(s):  
Fatemeh Pouromran ◽  
Srinivasan Radhakrishnan ◽  
Sagar Kamarthi
Keyword(s):  
Machine Learning ◽  
Pain Intensity ◽  
Learning Model ◽  
Support Vector ◽  
Heat Pain ◽  
Learning Models ◽  
Intensity Estimation ◽  
Machine Learning Model ◽  
Physiological Sensors ◽  
Machine Learning Models

In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.

scholarly journals Comparing machine learning and ensemble learning in the field of football

2019 ◽  
Vol 9 (5) ◽  
pp. 4321
Author(s):  
Shuaib Khan ◽  
Kirubanand V. B
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Learning Model ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Model ◽  
Vector Machines ◽  
The Right ◽  
Enormous Number ◽  
Machine Learning Models

Football has been one of the most popular and loved sports since its birth on November 6th, 1869. The main reason for this is because it is highly unpredictable in nature. Predicting football matches results seems like the perfect problem for machine learning models. But there are various caveats such as picking the right features from an enormous number of available features.  There have been many models which have been applied to various football-related datasets. This paper aims to compare Support Vector Machines a machine learning model and XGBoost an Ensemble learning model and how Ensemble Learning can greatly improve the accuracy of the predictions.

Improving Logging Prediction on Imbalanced Datasets

2016 ◽  
Vol 7 (2) ◽  
pp. 43-71 ◽  
Author(s):  
Sangeeta Lal ◽  
Neetu Sardana ◽  
Ashish Sureka
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Class Imbalance ◽  
Learning Model ◽  
Learning Models ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Imbalance Problem ◽  
Machine Learning Model ◽  
Machine Learning Models

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.

scholarly journals Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 9797
Author(s):  
Solaf A. Hussain ◽  
Nadire Cavus ◽  
Boran Sekeroglu
Keyword(s):  
Machine Learning ◽  
Body Fat ◽  
Support Vector ◽  
Body Fat Percentage ◽  
Learning Models ◽  
Fat Percentage ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Hybrid Machine ◽  
Machine Learning Models

Obesity or excessive body fat causes multiple health problems and diseases. However, obesity treatment and control need an accurate determination of body fat percentage (BFP). The existing methods for BFP estimation require several procedures, which reduces their cost-effectivity and generalization. Therefore, developing cost-effective models for BFP estimation is vital for obesity treatment. Machine learning models, particularly hybrid models, have a strong ability to analyze challenging data and perform predictions by combining different characteristics of the models. This study proposed a hybrid machine learning model based on support vector regression and emotional artificial neural networks (SVR-EANNs) for accurate recent BFP prediction using a primary BFP dataset. SVR was applied as a consistent attribute selection model on seven properties and measurements, using the left-out sensitivity analysis, and the regression ability of the EANN was considered in the prediction phase. The proposed model was compared to seven benchmark machine learning models. The obtained results show that the proposed hybrid model (SVR-EANN) outperformed other machine learning models by achieving superior results in the three considered evaluation metrics. Furthermore, the proposed model suggested that abdominal circumference is a significant factor in BFP prediction, while age has a minor effect.

scholarly journals Technical note: how to rationally compare the performances of different machine learning models?

2018 ◽  
Author(s):  
Terazima Maeda
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Technical Note ◽  
Learning Models ◽  
Specific Data ◽  
Machine Learning Model ◽  
Specific Prediction ◽  
Data Group ◽  
Machine Learning Models

Nowadays, there is a large number of machine learning models that could be used for various areas. However, different research targets are usually sensitive to the type of models. For a specific prediction target, the predictive accuracy of a machine learning model is always dependent to the data feature, data size and the intrinsic relationship between inputs and outputs. Therefore, for a specific data group and a fixed prediction mission, how to rationally compare the predictive accuracy of different machine learning model is a big question. In this brief note, we show how should we compare the performances of different machine models by raising some typical examples.

scholarly journals Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

2020 ◽  
Vol 9 (3) ◽  
pp. 875
Author(s):  
Young Suk Kwon ◽  
Moon Seong Baek
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Learning Model ◽  
Gradient Boosting ◽  
Learning Models ◽  
Suspected Infection ◽  
Machine Learning Model ◽  
Failure Assessment ◽  
Qsofa Score ◽  
Machine Learning Models

The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.

Machine learning models for Hg prospecting in the Almadén mining district

2021 ◽  
Author(s):  
Julio Alberto López-Gómez ◽  
Daniel Carrasco Pardo ◽  
Pablo Higueras ◽  
Jose María Esbrí ◽  
Saturnino Lorenzo
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Mining District ◽  
Learning Models ◽  
Geological Features ◽  
Machine Learning Model ◽  
Mineral Prospectivity ◽  
Data Point ◽  
The One ◽  
Machine Learning Models

&lt;p&gt;Traditionally, prospectivity models were designed using approaches mainly based on expert judgement. These models have been widely applied and they are also known as knowledge-driven prospectivity models (see Harris et al. (2015)). Currently, artificial intelligence approaches, especially machine learning models, are being applied to build prospectivity models since they have been proven to be successful in many other domains (see Sun et al., 2019 and Guerra Prado et al., 2020). They are also known as data-driven prospectivity models. Machine learning models allow to learn from data repositories in order to extract and detect relationships from the data to predict new instances.&lt;/p&gt;&lt;p&gt;In this work, a geological dataset was collected by a team of expert geologists. The data collected includes the geographical coordinates as well as several geological features of points belonged to seventy-seven different mercury deposits in the Almad&amp;#233;n mining district. The resulting dataset is composed by a total of 24798 points and 24 attributes for each point. In particular, we have collected geological and mining-related data regarding the Almad&amp;#233;n mercury (Hg) mining district; these data include the location of the several Hg mineralizations, including their typology, size, mineralogy, and stratigraphic position, as well as other information associated to the metallogenetic model set up by Hern&amp;#225;ndez et al. (1999).&lt;/p&gt;&lt;p&gt;Later, few machine learning models are built to select the one which offers the best results. The aim of this work is twofold: on the one hand, it is intended to build a machine learning model capable of, given the geological features of a data point, to determine the mercury deposit to which it belongs. On the other hand, the aim is to build a machine learning model capable of, given the geological features of a data point, to identify the kind of deposit to which it belongs. The experiments conducted in this work have been properly designed, validating the results obtained using statistical techniques.&lt;/p&gt;&lt;p&gt;Finally, the models built in this work will allow to generate mercury prospectivity maps. The final aim of this process is to get and train a system able to perform antimony prospection in the nearby Guadalmez syncline.&lt;/p&gt;&lt;p&gt;This work was funded by the ANR (ANR-19-MIN2-0002-01), the AEI (MICIU/AEI/REF.: PCI2019-103779) and author&amp;#8217;s institutions in the framework of the ERA-MIN2 AUREOLE project.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Guerra Prado E.M.; de Souza Filho C.R.; Carranza E.M.; Motta J.G. (2020). Modeling of Cu-Au prospectivity in the Caraj&amp;#225;s mineral province (Brasil) through machine learning: Dealing with embalanced training data.&lt;/p&gt;&lt;p&gt;Harris, J.R.; Grunsky, E.; Corrigan, D. (2015). Data- and knowledge-driven mineral prospectivity maps for Canda&amp;#8217;s North.&lt;/p&gt;&lt;p&gt;Hern&amp;#225;ndez, A.; J&amp;#233;brak, M.; Higueras, P.; Oyarzun, R.; Morata, D.; Munh&amp;#225;, J. (1999). The Almad&amp;#233;n mercury mining district, Spain. Mineralium Deposita, 34: 539-548.&lt;/p&gt;&lt;p&gt;Sun, T.; Chen, F.; Zhong, L.; Liu, W.; Wang, Y. (2019). GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China.&lt;/p&gt;

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 178-208
Author(s):  
S. Sasikala ◽  
S. J. Subhashini ◽  
P. Alli ◽  
J. Jane Rubel Angelina
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Medical Imaging ◽  
Learning Model ◽  
Learning Models ◽  
Driving System ◽  
Machine Learning Model ◽  
Car Driving ◽  
Machine Learning Models

Machine learning is a technique of parsing data, learning from that data, and then applying what has been learned to make informed decisions. Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way, but it has different capabilities. The main difference between deep and machine learning is, machine learning models become well progressively, but the model still needs some guidance. If a machine learning model returns an inaccurate prediction, then the programmer needs to fix that problem explicitly, but in the case of deep learning, the model does it by itself. Automatic car driving system is a good example of deep learning. On other hand, Artificial Intelligence is a different thing from machine learning and deep learning. Deep learning and machine learning both are the subsets of AI.

A Machine Learning Approach to Predict the Performance of Refrigerator and Air Conditioning Using Gaussian Process Regression and Support Vector Methods

2021 ◽  
Vol 14 ◽  
Author(s):  
Harinarayan Sharma ◽  
Sonam Kumari ◽  
Aniket K. Dutt ◽  
Pawan Kumar ◽  
Mamookho E. Makhatha
Keyword(s):  
Machine Learning ◽  
Air Conditioning ◽  
Gaussian Process Regression ◽  
Coefficient Of Performance ◽  
Experimental Result ◽  
Support Vector ◽  
Nano Particle ◽  
Learning Models ◽  
Machine Learning Model ◽  
Machine Learning Models

Aim: Develop machine learning models for the performance of refrigerator and airconditioning system. Background: The Coefficient Of Performance (COP) of Refrigerator and Air-Conditioning (RAC) is a complex function of evaporative temperature and concentration of nano-particle in lubricants. In recent years, researchers focus on experimental study for improvement of COP. Further, few researchers applied simulation techniques such as fuzzy system, Artificial Neural Network (ANN), simulated annealing, etc. to the Vapour Compression Refrigeration (VCR) cycle. There is a scarcity of modeling research work for the performance of RAC system. Objective: The study aims to develop the machine learning predictive models for the performance of refrigerator and air-conditioning system using experimental data. Methods: The experiment was performed on VCR system to determine COP. Three different concentration of lubricants (added 0.5, 1.0 and 1.5g nano-TiO2 particle on 1 liter of Polyolester (POE) oil) were used. The experimentally calculated COP was used to train and test the machine learning models. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) methods were applied to develop the models. Results: The experimental result reveals that the COP increases with increasing the concentration (of nano particles) at a given temperature. The addition of 0.5 and 1.0g TiO2 in the POE oil shows better rate of increment in the COP in comparison to addition of 1.5g TiO2 in the POE oil. Machine learning models using GPR and SVR with RBF kernel function is the most appropriate machine learning model for the nonlinear relationship between the output parameter (COP) and the input parameter (evaporative temperature and concentration of TiO2). Conclusion: The present study was conducted to investigate the machine learning approaches for performance of RAC system using experimental data sets. The experimental result shows that R134a and TiO2-POE nanolubricant work efficiently and the coefficient of performance of VCR system increases with concentration of nano-particle. The developed model performance is compared using coefficient of correlation and RSME values. After comparison, it is concluded that RBF based GPR model is the best fit machine learning model to predict the COP in the context of any other model for this data set.

Hybrid Modeling In Unconventional Reservoirs To Forecast Estimated Ultimate Recovery

2021 ◽  
Author(s):  
Cenk Temizel ◽  
Celal Hakan Canbaz ◽  
Karthik Balaji ◽  
Ahsen Ozesen ◽  
Kirill Yanidis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Integrated Approach ◽  
Learning Model ◽  
Hybrid Modeling ◽  
Unconventional Reservoirs ◽  
Learning Models ◽  
Production Forecasting ◽  
Machine Learning Model ◽  
Machine Learning Models ◽  
Estimated Ultimate Recovery

Abstract Machine learning models have worked as a robust tool in forecasting and optimization processes for wells in conventional, data-rich reservoirs. In unconventional reservoirs however, given the large ranges of uncertainty, purely data-driven, machine learning models have not yet proven to be repeatable and scalable. In such cases, integrating physics-based reservoir simulation methods along with machine learning techniques can be used as a solution to alleviate these limitations. The objective of this study is to provide an overview along with examples of implementing this integrated approach for the purpose of forecasting Estimated Ultimate Recovery (EUR) in shale reservoirs. This study is solely based on synthetic data. To generate data for one section of a reservoir, a full-physics reservoir simulator has been used. Simulated data from this section is used to train a machine learning model, which provides EUR as the output. Production from another section of the field with a different range of reservoir properties is then forecasted using a physics-based model. Using the earlier trained model, production forecasting for this section of the reservoir is then carried out to illustrate the integrated approach to EUR forecasting for a section of the reservoir that is not data rich. The integrated approach, or hybrid modeling, production forecasting for different sections of the reservoir that were data-starved, are illustrated. Using the physics-based model, the uncertainty in EUR predictions made by the machine learning model has been reduced and a more accurate forecasting has been attained. This method is primarily applicable in reservoirs, such as unconventionals, where one section of the field that has been developed has a substantial amount of data, whereas, the other section of the field will be data starved. The hybrid model was consistently able to forecast EUR at an acceptable level of accuracy, thereby, highlighting the benefits of this type of an integrated approach. This study advances the application of repeatable and scalable hybrid models in unconventional reservoirs and highlights its benefits as compared to using either physics-based or machine-learning based models separately.

Sign in / Sign up

Export Citation Format

Share Document