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.
Highlights
Machine learning and artificial intelligence growth are improving many research projects in the medical field from more than a decade with an approach based on the integration of pre-existing data to make a diagnosis, take a decision and predict results in a reduced time lapse.The K-Nearest Neighbor (KNN) is one of the most used and successful types of machine learning [1, 2].The k-NN algorithm is the simplest machine learning algorithm and it is good for small datasets
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 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
Summary
The K-Nearest Neighbor (KNN) is one of the most used and successful types of machine learning [1, 2].The k-NN algorithm is the simplest machine learning algorithm and it is good for small datasets. A model is built on the training data without using any model for fitting. It consists only of storing the training dataset. The model is able to make a prediction for a new data point, unseen data. The algorithm is able to find the closest data points in the training dataset. If a model is able to make accurate predictions on unseen data, it is able to generalize from the training set to the test set
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Global Journal of Engineering and Technology Advances
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
