Machine learning models applied in analyzing breast cancer classification accuracy

Diagnosing Breast Cancer by Machine Learning

Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultim...

multiple imputation in survival models: applied on breast cancer data

Applying Different Machine Learning Models to Predict Breast Cancer Risk

In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of ...

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologi...

Insights from Machine Learning Applied to Human Visual Classification

We attempt to understand visual classification in humans using both psychophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classified the faces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Pri...