Machine Learning and Its Applications in Healthcare

EasyChair Preprint 13561

Abstract

Machine learning, a subfield of artificial intelligence, has gained significant attention in recent years for its potential to revolutionize healthcare. This abstract provides an overview of machine learning and its applications in the healthcare industry.

Machine learning refers to the ability of computer systems to learn and improve from data without being explicitly programmed. It involves developing and applying algorithms that enable computers to automatically analyze and interpret complex patterns and relationships within large datasets. In healthcare, machine learning techniques can potentially transform various aspects of medical practice, including disease diagnosis, treatment optimization, and public health surveillance.

One of the primary applications of machine learning in healthcare is disease diagnosis and prognosis. By leveraging advanced algorithms, machine learning can analyze medical images such as X-rays, MRIs, and histopathology slides to assist radiologists and pathologists in detecting abnormalities and making accurate diagnoses. Additionally, machine learning models can be trained on clinical data to predict disease outcomes and assist healthcare providers in making informed decisions about treatment plans.

Keyphrases: Disease Surveillance, data analysis, data collection, data interpretation, public health surveillance, syndromic surveillance

@booklet{EasyChair:13561,
  author    = {Godwin Olaoye},
  title     = {Machine Learning and Its Applications in Healthcare},
  howpublished = {EasyChair Preprint 13561},
  year      = {EasyChair, 2024}}