Comparative Analysis of Breast and Prostate Cancer Prediction Using Machine Learning Techniques

Around the whole world, cancer is the most life-threatening disease. Basically, cancer can arise in any tissue of the body, and while each variety of cancer has unique characteristics, the fundamental processes that might cause cancer are highly common in all disease types. Breast cancer is one of the most ubiquitous types of cancer in females. In males, prostate cancer is the most dangerous during recent years. This study focuses on breast cancer as well as on prostate cancer in the direction of their early predictions. For early prediction, eight classification models had been used such as logistic regression (LR), Naive Bayes (NB), decision tree (DT), stochastic gradient descent (SGD), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). This work includes three different datasets for research analysis of breast and prostate cancer predictions. Two datasets for breast cancer (Coimbra and Wisconsin) and one for prostate cancer are taken from UCI and Kaggle repository, respectively. For improving the results of prediction, the normalization technique and feature selection method had been used in this paper. Performance in terms of accuracy, precision, recall, Fl-score, and curves of each classifier are analyzed in this study. Most of the classifiers did well after using the feature selection method (ANOVA). In the case of Breast Cancer Coimbra, KNN give good results with 80% accuracy in both the cases with or without using feature selection. Logistic regression with feature selection doing the best work on Wisconsin Breast Cancer with 99% accuracy. There are four classifiers (SVM, RF, DT, and SGD) which gives highest accuracy (97%) on prostate cancer.