Detecting Breast Cancer through Blood Analysis Data using Classification Algorithms

Breast cancer is the second major cause of death, and it accounts for 16% of all cancer deaths worldwide. Most of the methods for detecting breast cancer such as mammography are very expensive and difficult to interpret. There are also limitations like cumulative radiation exposure, over-diagnosis, and false positives and negatives in women with a dense breast that pose certain uncertainties in the high-risk populations. The objective of this work is to create a model that detects breast cancer through blood analysis data using the classification algorithms. This serves as a complement to the expensive methods. High-ranking features are extracted from the dataset. The KNN, SVM, and J48 algorithms are used as the training platform in order to classify 116 instances. Furthermore, the 10-fold cross-validation and holdout procedures are used coupled with changing of random seed. The results obtained show that the KNN algorithm has the highest and best accuracies of 89.99% and 85.21% for the cross-validation and holdout procedures, respectively. This is followed by the J48 algorithm with accuracies of 84.65% and 75.65% for the two procedures, respectively. The SVM algorithm has the accuracies of 77.58 and 68.69%, respectively. Although, it has also been discovered that the blood glucose level is a major determinant in detecting the breast cancer, it has to be combined with other attributes to make decisions as a result of other health issues like diabetes. With the results obtained, women are advised to do regular check-ups including blood analysis to know which blood components are required to be worked on in order to prevent breast cancer based on the model generated in this work.