Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning

Abstract

Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data.

Death/Recovery Prediction for Covid-19 Patients using Machine Learning

“…Recently, Artificial intelligence models have achieved a great success in the medical imaging due to its high capability of feature extraction [1], [2]. In addition, Artificial intelligence is also used to predict diseases in order to early detect the disease so that the treatment will be much easier and much safer [3].…”

“…The "Novel Corona Virus 2019 Dataset" available on Kaggle has over 10k of data from different patients in different countries with different attributes 2 . The main goal of this work is to predict the probability of death, recovery, stable or severe by using three machine learning algorithms.…”