%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 }