scholarly journals Machine Learning For Prognosis of Life Expectancy and Diseases

2019 ◽  
Vol 8 (10) ◽  
pp. 1765-1771
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Life Expectancy ◽  
Multiple Linear Regression ◽  
Machine Learning Algorithms ◽  
Economic Factors ◽  
Average Life Expectancy ◽  
Average Percentage ◽  
Hiv Aids

Longevity depends on various facets such as economic growth of the country, along with the health innovations of the region. Along with the prophecy of existence, we also figure out how sensitive a particular mainland is to few chronic diseases. These factors have a robust impact on the potential life span of the population. We study the biological and economical aspects of continents and their countries to predict the life expectancy of the population and to perceive the probability of the continent possessing long standing diseases like measles, HIV/AIDS, etc. Our research is conducted on the theory that exhibits the dependency or correlation of life expectancy with the various factors which includes the health factors as well as the economic factors. Two Machine learning algorithms simple linear regression, multiple linear regression are used for predicting the expectancy of life over different continents, whereas, decision tree algorithm, random forest algorithm, and were applied to classify the likelihood of occurrence of the disease. On comparing and contrasting various algorithms, we can infer that, multiple linear regression produces the most accurate results as to what the average life expectancy of the population would be given the current features of the continent like the adult mortality rate, alcohol consumption rate, infant deaths, the GDP of the country, average percentage expenditure of the population on health care and treatments, schooling rate, and other such features. On the other hand, we study five diseases namely, HIV/AIDS, measles, diphtheria, hepatitis B and polio. The experiment concluded that, on majority, random forest produces better results of classification based on the economic factors of the combination of various countries of different continents

scholarly journals Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

2019 ◽  
Vol 8 (9) ◽  
pp. 382 ◽  
Author(s):  
Marcos Ruiz-Álvarez ◽  
Francisco Alonso-Sarria ◽  
Francisco Gomariz-Castillo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Air Temperature ◽  
Cross Validation ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R 2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

scholarly journals Application of Machine Learning Algorithms in Predicting Pyrolytic Analysis Result

2021 ◽  
Vol 931 (1) ◽  
pp. 012013
Author(s):  
Le Thi Nhut Suong ◽  
A V Bondarev ◽  
E V Kozlova
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Decision Tree ◽  
Multiple Linear Regression ◽  
Oil And Gas ◽  
Polynomial Regression ◽  
Source Rocks ◽  
Regression Algorithms ◽  
Before And After

Abstract Geochemical studies of organic matter in source rocks play an important role in predicting the oil and gas accumulation of any territory, especially in oil and gas shale. For deep understanding, pyrolytic analyses are often carried out on samples before and after extraction of hydrocarbon with chloroform. However, extraction is a laborious and time-consuming process and the workload of laboratory equipment and time doubles. In this work, machine learning regression algorithms is applied for forecasting S2ex based on the pyrolytic analytic result of non-extracted samples. This study is carried out using more than 300 samples from 3 different wells in Bazhenov formation, Western Siberia. For developing a prediction model, 5 different machine learning regression algorithms including Multiple Linear Regression, Polynomial Regression, Support vector regression, Decision tree and Random forest have been tested and compared. The performance of these algorithms is examined by R-squared coefficient. The data of the X2 well was used for building a model. Simultaneously, this data is divided into 2 parts – 80% for training and 20% for checking. The model also was used for prediction of wells X1 and X3. Then, these predictive results were compared with the real results, which had been obtained from standard experiments. Despite limited amount of data, the result exceeded all expectations. The result of prediction also showcases that the relationship between before and after extraction parameters are complex and non-linear. The proof is R2 value of Multiple Linear Regression and Polynomial Regression is negative, which means the model is broken. However, Random forest and Decision tree give us a good performance. With the same algorithms, we can apply for prediction all geochemical parameters by depth or utilize them for well-logging data.

scholarly journals Interpolation of Instantaneous Air Temperature Using Geographical and MODIS Derived Variables with Machine Learning Techniques

2019 ◽  
Author(s):  
Marcos Ruiz-Álvarez ◽  
Francisco Alonso-Sarría ◽  
Francisco Gomariz-Castillo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Linear Regression ◽  
Air Temperature ◽  
Satellite Data ◽  
Multivariate Linear Regression ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machine and Random Forest, are compared with Multivariate Linear Regression, TVX and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using four different statistics on a daily basis allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest with residual kriging produces the best results (R$^2$=0.612 $\pm$ 0.019, NSE=0.578 $\pm$ 0.025, RMSE=1.068 $\pm$ 0.027, PBIAS=-0.172 $\pm$ 0.046), whereas TVX produces the least accurate results. The environmental conditions in the study area are not really suited to TVX, moreover this method only takes into account satellite data. On the other hand, regression methods (Support Vector Machine, Random Forest and Multivariate Linear Regression) use several parameters that are easily calculated from a Digital Elevation Model, adding very little difficulty to the use of satellite data alone. The most important variables in the Random Forest Model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day.

scholarly journals Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study

2019 ◽  
Vol 10 (1) ◽  
pp. 129 ◽  
Author(s):  
Jonghak Lee ◽  
Taekwan Yoon ◽  
Sangil Kwon ◽  
Jongtae Lee
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Mean Square Error ◽  
Model Evaluation ◽  
Traffic Accident ◽  
Negative Binomial ◽  
Machine Learning Algorithms ◽  
Mean Square ◽  
Road Geometry

There have been numerous studies on traffic accidents and their severity, particularly in relation to weather conditions and road geometry. In these studies, traditional statistical methods have been employed, such as linear regression, logistic regression, and negative binomial regression modeling, which are the most common linear and non-linear regression analysis methods. In this research, machine learning architecture was applied to this problem using the random forest, artificial neural network, and decision tree techniques to ascertain the strengths and weaknesses of these methods. Three data sets were used: road geometry data, precipitation data, and traffic accident data over nine years corresponding to the Naebu Expressway, which is located in Seoul, Korea. For the model evaluation, three measures were employed: the out-of-bag estimate of error rate (OOB), mean square error (MSE), and root mean square error (RMSE). The low mean OOB, MSE, and RMSE observed in the results obtained using the proposed random forest model demonstrate its accuracy.

scholarly journals Predicting Future Products Rate using Machine Learning Algorithms

2020 ◽  
Vol 12 (5) ◽  
pp. 41-51
Author(s):  
Shaimaa Mahmoud ◽  
◽  
Mahmoud Hussein ◽  
Arabi Keshk
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Data Set ◽  
Squared Error

Opinion mining in social networks data is considered as one of most important research areas because a large number of users interact with different topics on it. This paper discusses the problem of predicting future products rate according to users’ comments. Researchers interacted with this problem by using machine learning algorithms (e.g. Logistic Regression, Random Forest Regression, Support Vector Regression, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression and Decision Tree). However, the accuracy of these techniques still needs to be improved. In this study, we introduce an approach for predicting future products rate using LR, RFR, and SVR. Our data set consists of tweets and its rate from 1:5. The main goal of our approach is improving the prediction accuracy about existing techniques. SVR can predict future product rate with a Mean Squared Error (MSE) of 0.4122, Linear Regression model predict with a Mean Squared Error of 0.4986 and Random Forest Regression can predict with a Mean Squared Error of 0.4770. This is better than the existing approaches accuracy.

scholarly journals Using Artificial Neural Networks to Improve CFS Week 3-4 Precipitation and 2-Meter Air Temperature Forecasts

2021 ◽  
Author(s):  
Yun Fan ◽  
Vladimir Krasnopolsky ◽  
Huug van den Dool ◽  
Chung-Yu Wu ◽  
Jon Gottschalck
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Forecast Accuracy ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Nonlinear Features ◽  
Model Output Statistics ◽  
High Level

AbstractForecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, post-processing methods, from simple bias correction methods to more complicated multiple linear regression-based Model Output Statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as Week 3-4 forecasts.In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve Week 3-4 precipitation and 2-meter temperature forecasts made by the NOAA NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and co-variabilities hidden within the multi-dimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model Week 3-4 precipitation and 2-meter temperature forecasts. The results show that to some extent neural network techniques can significantly improve the Week 3-4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

scholarly journals Application of Data Mining Algorithms for Dementia in People with HIV/AIDS

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Luana Ibiapina Cordeiro Calíope Pinheiro ◽  
Maria Lúcia Duarte Pereira ◽  
Marcial Porto Fernandez ◽  
Francisco Mardônio Vieira Filho ◽  
Wilson Jorge Correia Pinto de Abreu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Principal Component ◽  
Machine Learning Algorithms ◽  
Hiv Aids

Dementia interferes with the individual’s motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

Rice Crop Yield Prediction Using Multi-Level Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4280-4286
Author(s):  
G. L. Anoop ◽  
C. Nandini
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Decision Tree ◽  
Multiple Linear Regression ◽  
Crop Yield ◽  
Machine Learning Algorithms ◽  
Rice Crop ◽  
Machine Learning Techniques ◽  
Indian Government ◽  
Regression Methods

Agriculture and allied production contributes to Indian economy and food security of India. Crop yield predictive model will help farmers and agriculture department and organization to take better decisions. In this paper we are proposingmulti-level, machine learning algorithms to predict rice crop yield. Here, data were collected from Indian Government website for 4 districts of Karnataka, i.e., Mysore, Mandya Raichur and Koppal, these data were publically available. In our proposed method initially, we have performed data pre-processing using z-score, normalization and Standardizing residuals on collected data, then multilevel decision tree and multilevel multiple linear regression methods are presented to predict the rice crop yield and evaluated the performance of both. The experimental results shows that the multiple linear regression is accurate than the decision tree technique. This prediction will guide the farmer to make better decision to gain better yield and for their livelihood in particular temperature or climatic scenario.

scholarly journals Machine Learning in Stock Price Forecast

2020 ◽  
Vol 214 ◽  
pp. 02050
Author(s):  
Zhen Sun ◽  
Shangmei Zhao
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Forecast ◽  
Closing Price ◽  
The Difference ◽  
Lstm Network ◽  
Better Than

This paper analyzed and compared the forecast effect of three machine learning algorithms (multiple linear regression, random forest and LSTM network) in stock price forecast using the closing price data of NASDAQ ETF and data of statistical factors. The test results show that the prediction effect of the closing price data is better than that of statistical factors, but the difference is not significant. Multiple linear regression is most suitable for stock price forecast. The second is random forest, which is prone to overfitting. The forecast effect of LSTM network is the worst and the values of RMSE and MAPE were the highest. The forecast effect of future stock price using closing price of NASDAQ ETF is better than that using statistical factors, but the difference is not significant.

scholarly journals Influenza Prediction: Analyzing Machine Learning Algorithms

2020 ◽  
Vol 9 (1) ◽  
pp. 14-18
Author(s):  
Sapna Yadav ◽  
Pankaj Agarwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Random Forests ◽  
Ridge Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Digital Data ◽  
Support Vector ◽  
Lasso Regression

Analyzing online or digital data for detecting epidemics is one of the hot areas of research and now becomes more relevant during the present outbreak of Covid-19. There are several different types of the influenza virus and moreover they keep evolving constantly in the same manner the COVID-19 virus has done. As a result, they pose a greater challenge when it comes to analyzing them, predicting when, where and at what degree of severity it will outbreak during the flu season across the world. There is need for greater surveillance to both seasonal and pandemic influenza to ensure the health and safety of the mankind. The objective of work is to apply machine learning algorithms for building predictive models that can predict where the occurrence, peak and severity of influenza in each season. For this work we have considered a freely available dataset of Ireland which is recorded for the duration of 2005 to 2016. Specifically, we have tested three ML Algorithms namely Linear Regression, Support Vector Regression and Random Forests. We found Random Forests is giving better predictive results. We also conducted experiment through weka tool and tested Zero R, Linear Regression, Lazy Kstar, Random Forest, REP Tree, Multilayer Perceptron models. We again found the Random Forest is performing better in comparison to all other models. We also evaluated other regression models including Ridge Regression, modified Ridge regression, Lasso Regression, K Neighbor Regression and evaluated the mean absolute errors. We found that modified Ridge regression is producing minimum error. The proposed work is inclined towards finding the suitability & appropriate ML algorithm for solving this problem on Flu.

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