TY  - JOUR
A1  - Hassannataj Joloudari, Javad
A1  - Hassannataj Joloudari, Edris
A1  - Saadatfar, Hamid
A1  - GhasemiGol, Mohammad
A1  - Razavi, Seyyed Mohammad
A1  - Mosavi, Amir
A1  - Nabipour, Narjes
A1  - Shamshirband, Shahaboddin
A1  - Nadai, Laszlo
T1  - Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model
JF  - International Journal of Environmental Research and Public Health,  IJERPH
N2  - Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - Deep learning
KW  - coronary artery disease
KW  - heart disease diagnosis
KW  - health informatics
KW  - data science
KW  - big data
KW  - predictive model
KW  - ensemble model
KW  - random forest
KW  - industry 4.0
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200213-40819
UR  - https://www.mdpi.com/1660-4601/17/3/731
VL  - 2020
IS  - Volume 17, Issue 3, 731
PB  - MDPI
ER  - 
TY  - JOUR
A1  - Kargar, Katayoun
A1  - Samadianfard, Saeed
A1  - Parsa, Javad
A1  - Nabipour, Narjes
A1  - Shamshirband, Shahaboddin
A1  - Mosavi, Amir
A1  - Chau, Kwok-Wing
T1  - Estimating longitudinal dispersion coefficient in natural streams using empirical models and machine learning algorithms
JF  - Engineering Applications of Computational Fluid Mechanics
N2  - The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.
KW  - Maschinelles Lernen
KW  - Gaussian process regression
KW  - longitudinal dispersion coefficient
KW  - M5 model tree
KW  - random forest
KW  - support vector regression
KW  - rivers
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200128-40775
UR  - https://www.tandfonline.com/doi/full/10.1080/19942060.2020.1712260
VL  - 2020
IS  - Volume 14, No. 1
SP  - 311
EP  - 322
PB  - Taylor & Francis
ER  -