Medical image prediction for the diagnosis of breast cancer and comparing machine learning algorithms: SVM, logistic regression, random forest and decision tree to measure accuracy of prediction

Breast cancer is a major concern to middle-aged women across the globe, and it is now the second leading cause of cancer mortality in women. SVM, KNN, and Random Forest, Decision Trees are the primary focus of my research. Samples from the University of California, Irvine’s Machine Learning Laboratory total 569. SVM, Decision Tree, Random Forest, and KNN are used to classify the samples as either benign or malignant based on the appearance of the cells. G-power calculations are used to determine the number of samples needed for this study. The analysis’s minimum power is set at 0.8, while the tolerated error level is set at 0.5. As compared to SVM, KNN, and Decision Tree, Random Forest predicts a 95 percent accuracy rate (compared to 92 percent, 90 percent, and 88 percent, respectively). This system has a significance of 0.22. Random Forest outperforms SVM, Decision Tree, and KNN in the identification of breast cancer in this innovative picture prediction.