Breast cancer prediction using different machine learning methods applying multi factors.

Objective: Breast cancer (BC) is a multifactorial disease and is one of the most common cancers globally. This study aimed to compare different machine learning (ML) techniques to develop a comprehensive breast cancer risk prediction model based on features of various factors.

Methods: The population sample contained 810 records (115 cancer patients and 695 healthy individuals).

Deep Learning for Acute Myeloid Leukemia Diagnosis.

By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widely used in cancer diagnosis and screening since it is able to measure gene expression levels. Analyzing them by using common statistical methods is not suitable because of the high gene expression data dimensions.