Machine learning and regression analysis for diagnosis of bruxism by using EMG signals of jaw muscles

The paper analyzes the socio-economic and demographic indicators of life expectancy in the countries of the world. Methods of regression analysis and machine learning are used. Statistically significant indicators that affect life expectancy around the world have been identified. When analyzing the data using machine learning methods, 13 of the 14 analyzed indicators were statistically significant. Significant indicators, in addition to those selected in the regression analysis, were 3: the under-five infant mortality rate (per 1,000 live births), the Net Barter Terms of Trade Index (2000 = 100), and Imports of goods and services (in % of GDP) (in the regression analysis, only the infant death rate was significant). In addition, it should be noted that there is a significant decrease in the under-five infant mortality rate (per 1,000 live births) for the EU, CIS and South-East Asian countries compared to the border set in the study for all countries: 4.65 vs. 34.9, a decrease in the birth rate from 2.785 to 1.85, a sharp increase in exports of goods and services: from 23.17 to 80.59, a halving in imports of goods and services, a drop in population growth from 2.105 to 0.85. The performed statistical analysis strongly supports the use of machine learning methods in identifying statistically significant relationships between various indicators that characterize the development of countries, if there are gaps in the data.

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Cycling performance prediction based on cadence analysis by using multiple regression

Journal of Physics Conference Series ◽

10.1088/1742-6596/2107/1/012058 ◽

2021 ◽

Vol 2107 (1) ◽

pp. 012058

Author(s):

Sukhairi Sudin ◽

Azizi Naim Abdul Aziz ◽

Fathinul Syahir Ahmad Saad ◽

Nurul Syahirah Khalid ◽

Ismail Ishaq Ibrahim

Keyword(s):

Machine Learning ◽

Heart Rate ◽

Regression Analysis ◽

Linear Regression ◽

Linear Relationship ◽

Multiple Regression ◽

Cycling Performance ◽

Continuous Output ◽

Independent Variable ◽

Prediction Problems

Abstract This project examined the influence of the cadence, speed, heart rate and power towards the cycling performance by using Garmin Edge 1000. Any change in cadence will affect the speed, heart rate and power of the novice cyclist and the changes pattern will be observed through mobile devices installed with Garmin Connect application. Every results will be recorded for the next task which analysis the collected data by using machine learning algorithm which is Regression analysis. Regression analysis is a statistical method for modelling the connection between one or more independent variables and a dependent (target) variable. Regression analysis is required to answer these types of prediction problems in machine learning. Regression is a supervised learning technique that aids in the discovery of variable correlations and allows for the prediction of a continuous output variable based on one or more predictor variables. A total of forty days’ worth of events were captured in the dataset. Cadence act as dependent variable, (y) while speed, heart rate and power act as independent variable, (x) in prediction of the cycling performance. Simple linear regression is defined as linear regression with only one input variable (x). When there are several input variables, the linear regression is referred to as multiple linear regression. The research uses a linear regression technique to predict cycling performance based on cadence analysis. The linear regression algorithm reveals a linear relationship between a dependent (y) variable and one or more independent (y) variables, thus the name. Because linear regression reveals a linear relationship, it determines how the value of the dependent variable changes as the value of the independent variable changes. This analysis use the Mean Squared Error (MSE) expense function for Linear Regression, which is the average of squared errors between expected and real values. Value of R squared had been recorded in this project. A low R-squared value means that the independent variable is not describing any of the difference in the dependent variable-regardless of variable importance, this is letting know that the defined independent variable, although meaningful, is not responsible for much of the variance in the dependent variable’s mean. By using multiple regression, the value of R-squared in this project is acceptable because over than 0.7 and as known this project based on human behaviour and usually the R-squared value hardly to have more than 0.3 if involve human factor but in this project the R-squared is acceptable.

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