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 - Saadatfar, Hamid A1 - Khosravi, Samiyeh A1 - Hassannataj Joloudari, Javad A1 - Mosavi, Amir A1 - Shamshirband, Shahaboddin T1 - A New K-Nearest Neighbors Classifier for Big Data Based on Efficient Data Pruning JF - Mathematics N2 - The K-nearest neighbors (KNN) machine learning algorithm is a well-known non-parametric classification method. However, like other traditional data mining methods, applying it on big data comes with computational challenges. Indeed, KNN determines the class of a new sample based on the class of its nearest neighbors; however, identifying the neighbors in a large amount of data imposes a large computational cost so that it is no longer applicable by a single computing machine. One of the proposed techniques to make classification methods applicable on large datasets is pruning. LC-KNN is an improved KNN method which first clusters the data into some smaller partitions using the K-means clustering method; and then applies the KNN for each new sample on the partition which its center is the nearest one. However, because the clusters have different shapes and densities, selection of the appropriate cluster is a challenge. In this paper, an approach has been proposed to improve the pruning phase of the LC-KNN method by taking into account these factors. The proposed approach helps to choose a more appropriate cluster of data for looking for the neighbors, thus, increasing the classification accuracy. The performance of the proposed approach is evaluated on different real datasets. The experimental results show the effectiveness of the proposed approach and its higher classification accuracy and lower time cost in comparison to other recent relevant methods. KW - Maschinelles Lernen KW - Machine learning KW - K-nearest neighbors KW - KNN KW - classifier KW - big data KW - clustering KW - cluster shape KW - cluster density KW - classification KW - reinforcement learning KW - data science KW - computation KW - artificial intelligence KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40996 UR - https://www.mdpi.com/2227-7390/8/2/286 VL - 2020 IS - volume 8, issue 2, article 286 PB - MDPI ER -