Analysis of Machine Learning Algorithms for Healthcare Applications

EasyChair Preprint 15185

Abstract

Machine learning (ML) algorithms have significantly advanced healthcare applications, offering robust solutions for disease diagnosis, patient prognosis, treatment personalization, and operational efficiency. This paper provides a comprehensive analysis of various ML algorithms used in healthcare, focusing on their methodologies, applications, strengths, and limitations. By evaluating these algorithms, we aim to identify the most suitable techniques for different healthcare scenarios and propose future research directions to enhance their efficacy. India has a vast healthcare system, yet the quality varies greatly between the public and private sectors as well as between rural and metropolitan areas. Machine learning is particularly influential in the development of a multimodal, highly sophisticated, automated biomedical data model. The use of machine learning in healthcare could improve the accuracy of disease diagnosis and save time by forecasting the device. This chapter examines the various healthcare industries and the ways in which machine learning can be applied to them.

Keyphrases: Disease Diagnosis, Healthcare, Treatment personalization, machine learning, prognosis

@booklet{EasyChair:15185,
  author    = {Senjuti Banerjee and Sudipta Hazra and Aaliya Nashat and Siddhartha Chatterjee and Lucky Adhikari},
  title     = {Analysis of Machine Learning Algorithms for Healthcare Applications},
  howpublished = {EasyChair Preprint 15185},
  year      = {EasyChair, 2024}}