scholarly journals Blood Biomarkers Predict Cardiac Workload Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Lan Shou ◽  
Wendy Wenyu Huang ◽  
Andrew Barszczyk ◽  
Si Jia Wu ◽  
Helen Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Protein Concentration ◽  
Health Management ◽  
Learning Algorithm ◽  
Rate Pressure Product ◽  
Cellular Phenotype ◽  
Blood Biomarkers ◽  
Standard Linear ◽  
Linear Regression Technique

Introduction. Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which an individual’s biochemical/cellular profile as characterized by a standard blood panel is predictive of rRPP, as well the importance of each blood biomarker in this prediction. Methods. We included data from 55,730 participants in this study with complete rRPP measurements and concurrently collected blood panel information from the Health Management Centre at the Affiliated Hospital of Hangzhou Normal University. We used the XGBoost machine learning algorithm to train a tree-based model and then assessed its accuracy on an independent portion of the dataset and then compared its performance against a standard linear regression technique. We further determined the predictive importance of each feature in the blood panel. Results. We found a fair positive correlation (Pearson r ) of 0.377 (95% CI: 0.375-0.378) between observed rRPP and rRPP predicted from blood biomarkers. By comparison, the performance for standard linear regression was 0.352 (95% CI: 0.351-0.354). The top three predictors in this model were glucose concentration, total protein concentration, and neutrophil count. Discussion/Conclusion. Blood biomarkers predict resting RPP when modeled in combination with one another; such models are valuable for studying the complex interrelations between resting cardiac workload and one’s biochemical/cellular phenotype.

scholarly journals Study on Influence of Range of Data in Concrete Compressive Strength with Respect to the Accuracy of Machine Learning with Linear Regression

2021 ◽  
Vol 11 (9) ◽  
pp. 3866
Author(s):  
Jun-Ryeol Park ◽  
Hye-Jin Lee ◽  
Keun-Hyeok Yang ◽  
Jung-Keun Kook ◽  
Sanghee Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Linear Regression ◽  
Coarse Aggregate ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Aggregate Size ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Concrete Compressive Strength

This study aims to predict the compressive strength of concrete using a machine-learning algorithm with linear regression analysis and to evaluate its accuracy. The open-source software library TensorFlow was used to develop the machine-learning algorithm. In the machine-earning algorithm, a total of seven variables were set: water, cement, fly ash, blast furnace slag, sand, coarse aggregate, and coarse aggregate size. A total of 4297 concrete mixtures with measured compressive strengths were employed to train and testing the machine-learning algorithm. Of these, 70% were used for training, and 30% were utilized for verification. For verification, the research was conducted by classifying the mixtures into three cases: the case where the machine-learning algorithm was trained using all the data (Case-1), the case where the machine-learning algorithm was trained while maintaining the same number of training dataset for each strength range (Case-2), and the case where the machine-learning algorithm was trained after making the subcase of each strength range (Case-3). The results indicated that the error percentages of Case-1 and Case-2 did not differ significantly. The error percentage of Case-3 was far smaller than those of Case-1 and Case-2. Therefore, it was concluded that the range of training dataset of the concrete compressive strength is as important as the amount of training dataset for accurately predicting the concrete compressive strength using the machine-learning algorithm.

Investigation of Influential Factors of Predicting Individuals' Use and Non-use of Fitness and Diet Apps on Smartphones: Application of the Machine Learning Algorithm (XGBoost)

2021 ◽  
Vol 45 (1) ◽  
pp. 111-124
Author(s):  
Jaehee Cho ◽  
Sehwan Kim ◽  
Gwangjin Jeong ◽  
Chonghye Kim ◽  
Ja-Kyoung Seo
Keyword(s):  
Machine Learning ◽  
Social Support ◽  
Social Influence ◽  
Health Management ◽  
Learning Algorithm ◽  
Media Use ◽  
Influential Factors ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Diverse Groups

Objectives: In this study, we aimed to find the influential factors in determining individuals' use and non-use of fitness and diet apps on smartphones. To this end, we focused on diverse groups of predictors that would significantly affect people's use and non-use of these apps. Methods: Overall, we considered 105 factors as potential predictors and included them in further analyses using a machine learning algorithm, XGBoost. The main reason for selecting this particular algorithm was that it had been known as one of the most accurate and popular algorithms for predicting consumer behaviors. Results: We found the accuracy score of those factors for predicting people's use and non-use of fitness and diet apps was approximately 71.3%. In particular, the most influential predictors were mainly related to social influence, media use, overeating, social support, health management, and attitudes toward exercise. Conclusion: These findings contribute to helping scholars and practitioners to develop more practical strategies of the implementation of fitness and diet apps.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

scholarly journals Automatic solution for solar cell photo-current prediction using machine learning

2021 ◽  
Vol 297 ◽  
pp. 01029
Author(s):  
Mohammed Azza ◽  
Jabran Daaif ◽  
Adnane Aouidate ◽  
El Hadi Chahid ◽  
Said Belaaouad
Keyword(s):  
Machine Learning ◽  
Solar Cell ◽  
Linear Regression ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Prediction Methods ◽  
Lasso Regression ◽  
Photo Current

In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

Overdischarge Detection and Prevention with Temperature Monitoring of Li-ion Batteries and Linear Regression-based Machine Learning

2021 ◽  
pp. 1-11
Author(s):  
Bing Li ◽  
Casey Jones ◽  
Vikas Tomar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Learning Algorithm ◽  
Linear Regression Analysis ◽  
Peak Temperature ◽  
Training Data ◽  
Battery Management System ◽  
Cell Temperature ◽  
Li Ion ◽  
Cutoff Voltage

Abstract This work focuses on the use of linear regression analysis-based machine learning for the prediction of the end of discharge of a commercial prismatic lithium (Li)-ion cell. The cell temperature was recorded during the cycling of Li-ion cells and the relation between the open circuit voltage and cell temperature was used in the development of the linear regression-based machine learning algorithm. The peak temperature was selected as the indicator of battery end of discharge. A battery management system using a pyboard microcontroller was constructed to monitor the temperature of the cell under test, and was also used to control a MOSFET that acted as a switch to disconnect the cell from the circuit. The method used an initial 10 charge and discharge cycles at a rate of 1C as the training data, then another charge and discharge cycle for the testing data. During the test cycling, the discharge was continued beyond the cutoff voltage to initiate an overdischarge while the temperature of the cell was continuously monitored. The experiment was performed on 3 different cells, and the overdischarge for each was secured within 0.1 V of the cutoff voltage. The results of these experiments show that a linear regression-based analysis can be implemented to detect an overdischarge condition of a cell based on the anticipated peak temperature during discharge.

scholarly journals A Comparative Study on House Price Prediction

2020 ◽  
Vol 6 (12) ◽  
pp. 103-107
Author(s):  
Akash Dagar and Shreya Kapoor
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
House Price ◽  
Machine Learning Algorithms ◽  
Random Forest Regression ◽  
Image Detection ◽  
Price Prediction ◽  
Normal Speech

Machine learning plays a major role from past years in image detection, spam reorganization, normal speech command, product recommendation and medical diagnosis. Present machine learning algorithm helps us in enhancing security alerts, ensuring public safety and improve medical enhancements. Due to increase in urbanization, there is an increase in demand for renting houses and purchasing houses. Therefore, to determine a more effective way to calculate house price accurately is the need of the hour. So, an effort has been made to determine the most accurate way of predicting house price by using machine learning algorithms: Multivariable Linear Regression, Decision Tree Regression and Random Forest Regression and it is determined that Multivariable Linear Regression has showed most accuracy and less error.

scholarly journals Global Warming Prediction in India using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 4061-4065
Keyword(s):  
Machine Learning ◽  
Global Warming ◽  
Linear Regression ◽  
Greenhouse Gases ◽  
Learning Algorithm ◽  
Regression Tree ◽  
Global Temperature ◽  
Support Vector ◽  
Data Set ◽  
Average Annual Temperature

Long term global warming prediction can be of major importance in various sectors like climate related studies, agricultural, energy, medical and many more. This paper evaluates the performance of several Machine Learning algorithm (Linear Regression, Multi-Regression tree, Support Vector Regression (SVR), lasso) in problem of annual global warming prediction, from previous measured values over India. The first challenge dwells on creating a reliable, efficient statistical reliable data model on large data set and accurately capture relationship between average annual temperature and potential factors such as concentration of carbon dioxide, methane, nitrous oxide. The data is predicted and forecasted by linear regression because it is obtaining the highest accuracy for greenhouse gases and temperature among all the technologies which can be used. It was also found that CO2 is the plays the role of major contributor temperature change, followed by CH4, then by N20. After seeing the analysed and predicted data of the greenhouse gases and temperature, the global warming can be reduced comparatively within few years. The reduction of global temperature can help the whole world because not only human but also different animals are suffering from the global temperature.

scholarly journals Motion Detection and Prediction Using Machine Learning Algorithm

2020 ◽  
pp. 220-226
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Logistic Regression ◽  
Linear Regression ◽  
21St Century ◽  
Motion Detection ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Processing Data

Machine learning is a branch of Artificial Intelligence which is gaining importance in the 21st century with increasing processing speeds and miniaturization of sensors, the applications of Artificial Intelligence and cognitive technologies are growing rapidly. An array of ultrasonic sensors i.e., HCSR-04 is placed at different directions, collecting data for a particularinterval of a period during a particular day. The acquired sensor values are subjected to pre-processing, data analytics, and visualization. The prepared data is now split into test and train. A prediction model is designed using logistic regression and linear regression and checked for accuracy, F1 score, and precision compared.

scholarly journals Oxidation States of Binary Oxides from Data Analytics of the Electronic Structure

2018 ◽  
Author(s):  
Sergei Posysaev ◽  
Olga Miroshnichenko ◽  
Matti Alatalo ◽  
Duy Le ◽  
Talat S. Rahman
Keyword(s):  
Machine Learning ◽  
Electronic Structure ◽  
Linear Regression ◽  
Data Analytics ◽  
Learning Algorithm ◽  
Mixed Valence ◽  
Oxidation States ◽  
Machine Learning Algorithm ◽  
Binary Oxides ◽  
Mixed Valence Compounds

<p>A connection between the oxidation state (OS) and Bader charge has been missing so far. To our knowledge, all previous work tried to connect OS with Bader charges only with few compounds. The aim of this work was to find a dependency between OS and Bader charge, using <a>a large number of compounds from an open database</a>. We show that a <a>correlation indeed exists between OSs and Bader charges</a> using the simplest machine learning algorithm, linear regression. The applicability of determining OS by Bader charges in mixed-valence compounds and surfaces is considered.</p>

scholarly journals South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ibidun Christiana Obagbuwa ◽  
Ademola P. Abidoye
Keyword(s):  
Machine Learning ◽  
South Africa ◽  
Linear Regression ◽  
Learning Algorithm ◽  
Research Work ◽  
Data Repository ◽  
Learning Approach ◽  
Crime Data ◽  
Social Violence ◽  
Machine Learning Approach

South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. However, the two elements that pushed South Africa high in the crime rank are the rates of social violence and homicide. It was reported by Business Insider that South Africa is among the most top 15 ferocious nations on earth. By 1995, South Africa was rated the second highest in terms of murder. However, the crime rate has reduced for some years and suddenly rose again in recent years. Due to social violence and crime rates in South Africa, foreign investors are no longer interested in continuing or starting a business with the nation, and hence, its economy is declining. South Africa’s government is looking for solutions to the crime issue and to redeem the image of the country in terms of high crime ranking and boost the confidence of the investors. Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. The police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. This research work aimed at offering a solution to the problem by building a model that can predict crime. The machine learning approach shall be used to extract useful information from South Africa's nine provinces' crime data. A crime prediction system that can analyze and predict crime is proposed. To accomplish this, South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm (linear regression) was used to build a predictive model to analyze data and predict future crime. The appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses.

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