In this investigation, we have developed a graphical user interface application to perform the diagnostic of pathology on the column vertebral based on the Cluster K-Nearest Neighbor (CKNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 6 features and two (02) classes. Each class, abnormal and normal class consists of 210 instances, and 100 instances, respectively. A comparison of the performance of the test measurement under various test sizes (10%~50%) is carried out to predict the class label when the nearest neighbor k changes from 1 to 19. The results show that the accuracy depends on both independent parameters, the test size and k-neighbors, which gives better training accuracy than the test accuracy, in the range of 82.5% ~ 100% and 70%~84%, respectively. When k varies from 1 to 4, a higher training accuracy, larger than 90% is observed. While the test set shows a low accuracy in the range of 74% ~ 82.5%. Increasing the test size or/and k, does not affect significantly the accuracy. When k is larger 1, the training accuracy is approximately equal to 0.925±0.05, the test accuracy (except for k=6 and 17) is about 0.79±0.05. The prediction of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. The GUI can be useful to help the medical doctors to diagnostic the patient effectively to take a rapid decision and predict results in a reduced time lapse.
%0 Journal Article
%1 aissa_boudjella_2020_4478384
%A Boudjella, Aissa
%A Arab, Sarah
%A Boudjella, Manal Y.
%A Khiter, Sarah
%A Bellebna, Bachir
%D 2020
%J Global Journal of Engineering and Technology Advances
%K Vertebral column
%N 3
%P 020-028.
%R 10.30574/gjeta.2020.5.3.0107
%T Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)
%U http://gjeta.com/content/diagnostic-pathology-vertebral-column-machine-learning-cluster-k-nearest-neighbor-cknn-part
%V 5
%X In this investigation, we have developed a graphical user interface application to perform the diagnostic of pathology on the column vertebral based on the Cluster K-Nearest Neighbor (CKNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 6 features and two (02) classes. Each class, abnormal and normal class consists of 210 instances, and 100 instances, respectively. A comparison of the performance of the test measurement under various test sizes (10%~50%) is carried out to predict the class label when the nearest neighbor k changes from 1 to 19. The results show that the accuracy depends on both independent parameters, the test size and k-neighbors, which gives better training accuracy than the test accuracy, in the range of 82.5% ~ 100% and 70%~84%, respectively. When k varies from 1 to 4, a higher training accuracy, larger than 90% is observed. While the test set shows a low accuracy in the range of 74% ~ 82.5%. Increasing the test size or/and k, does not affect significantly the accuracy. When k is larger 1, the training accuracy is approximately equal to 0.925±0.05, the test accuracy (except for k=6 and 17) is about 0.79±0.05. The prediction of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. The GUI can be useful to help the medical doctors to diagnostic the patient effectively to take a rapid decision and predict results in a reduced time lapse.
@article{aissa_boudjella_2020_4478384,
abstract = {In this investigation, we have developed a graphical user interface application to perform the diagnostic of pathology on the column vertebral based on the Cluster K-Nearest Neighbor (CKNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 6 features and two (02) classes. Each class, abnormal and normal class consists of 210 instances, and 100 instances, respectively. A comparison of the performance of the test measurement under various test sizes (10%~50%) is carried out to predict the class label when the nearest neighbor k changes from 1 to 19. The results show that the accuracy depends on both independent parameters, the test size and k-neighbors, which gives better training accuracy than the test accuracy, in the range of [82.5% ~ 100%] and [70%~84%], respectively. When k varies from 1 to 4, a higher training accuracy, larger than 90% is observed. While the test set shows a low accuracy in the range of [74% ~ 82.5%]. Increasing the test size or/and k, does not affect significantly the accuracy. When k is larger 1, the training accuracy is approximately equal to 0.925±0.05, the test accuracy (except for k=6 and 17) is about 0.79±0.05. The prediction of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. The GUI can be useful to help the medical doctors to diagnostic the patient effectively to take a rapid decision and predict results in a reduced time lapse. },
added-at = {2021-01-30T06:26:36.000+0100},
author = {Boudjella, Aissa and Arab, Sarah and Boudjella, Manal Y. and Khiter, Sarah and Bellebna, Bachir},
biburl = {https://www.bibsonomy.org/bibtex/23278eba41c05f5c5fc3ffbddda1fd864/gjetajournal},
doi = {10.30574/gjeta.2020.5.3.0107},
interhash = {de53db156d5a00263e924ff88af30ea9},
intrahash = {3278eba41c05f5c5fc3ffbddda1fd864},
issn = {2582-5003},
journal = {{Global Journal of Engineering and Technology Advances}},
keywords = {Vertebral column},
month = dec,
number = 3,
pages = {020-028.},
timestamp = {2021-01-30T06:26:36.000+0100},
title = {Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)},
url = {http://gjeta.com/content/diagnostic-pathology-vertebral-column-machine-learning-cluster-k-nearest-neighbor-cknn-part},
volume = 5,
year = 2020
}