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.