IEEE

Early Disease Prediction using Supervised Machine Learning

Abstract: This research focuses on Healthcare and Artificial Intelligence, more especially Medical Data Analysis and Predictive Analytics. It analyses patient data for early disease diagnosis and ...
Frontiers

Establishment and comparison of three fear of progression risk prediction models for gynecological malignancies patients based on machine learning

Objective: This study applied the Society Ecosystems Theory to investigate Fear of Progression (FoP) prevalence and predictors in gynecological malignancy patients. By constructing and comparing three ...
Frontiers

Skin disease diagnosis using decision and feature level fusion of deep features

Introduction: Early skin disease diagnosis is essential and one of the challenging tasks for a dermatologist. Manual diagnosis by healthcare providers is subjective, costly, and may yield inconsistent ...
IEEE

Predictive Techniques in Machine Learning for Heart Disease

Abstract: Heart disease, also known as cardiovascular disease, is a broad term encompassing various conditions that affect the heart and blood vessels. These disorders can range from mild to ...
Rochester Institute of Technology

RIT researchers use machine learning to better understand the pathways of disease

Cancer, Alzheimer’s, and other diseases follow a pathway in the human body. It starts at the molecular and cellular levels, and through a series of complex interactions can lead to the development and ...