Survival prognosis in breast cancer using linear KNN comparing with novel Gaussian naive bayes.

The primary objective of this research project is to utilise a K-neighborhood to locate breast cancer tumours, after which the results will be compared to those obtained through the use of a Novel Gaussian Naive Bayes method for determining the degree to which accuracy is achieved in image processing. The K-neighborhood method is used to ten pictures selected at random from a larger than 300-picture collection. The same goal underlies the process of calculating accuracy values. For the purpose of the research, images of breast cancer were analysed using K-neighborhood machines and a Novel Gaussian Naive Bayes, each with 20 different sample sizes. Following the completion of the statistical analysis, the threshold of significance for establishing the accuracy of the computations was determined to be a p value of 0.05. When it comes to the identification of breast cancer, the mean accuracy of K-neighborhood is 89.3 percent, whereas the best that Gaussian naive bayes can do is 87.5 percent. We get to the conclusion that the K-neighborhood performs much better than the Novel Gaussian Naive Bayes algorithm. [ABSTRACT FROM AUTHOR]