Breast Cancer Prediction with Gaussian Process Using Anthropometric Parameters

In recent years, the breast cancer classification has piqued attention in the area of health-care informatics which is the second leading cause of mortality in women due to cancer. The spread of Breast Cancer growth and its high casualty has prodded a ton of research for contemplating its causes and medicines. The enormous number of genes and their entwining relations requires progressed AI models, instead of simple statistical and correlation analysis. Having the objective to propel the present status of knowledge concerning the early determination of breast cancer, we utilized Random Forest (RF), Logistic Regression(LR), Support Vector Machine(SVM), and Gaussian Process(GP) classifiers, joined with testing unique and novel biomarkers. This paper presents a correlation of these Machine Learning (ML) algorithms by estimating their order test exactness, and their sensitivity and specificity esteem. For the execution of the ML calculations, the dataset was divided into 80% for the training phase, and 20% for the testing phase. Results show that Gaussian Process performed well with 90% test exactness on the order task.