Integrative Predictive Analytics for Early Rheumatoid Arthritis Detection Using Clinical Parameters

EasyChair Preprint no. 12299

Abstract

Rheumatoid arthritis (RA) is a long-term inflammatory disease that causes inflammation in the joints, which can cause pain, stiffness, and restricted movement. Early detection and treatment are crucial to reducing the long-term effects on the quality of life of patients. Promising opportunities for enhancing RA classification and prediction models are presented by recent advancements in digital health, machine learning, techniques, and multi-omics data. A comprehensive framework for RA identification is established by merging information from multiple sources, including test results and patient-reported outcomes. Ethical considerations and validation methods ensure the dependability and ethical purity of our methodology. The use of this method might result in higher rates of early diagnosis and more customised therapy regimens, both of which would eventually enhance patient outcomes in the context of RA care.

Keyphrases: Artificial Intelligence (AI), Machine Learning(ML), Rheum AI (RHAI), Rheumatoid Arthritis (RA)

@Booklet{EasyChair:12299,
  author = {Kulvinder Singh and Ankit Rawat and Nishant Singh and Rajiv Paul},
  title = {Integrative Predictive Analytics for Early Rheumatoid Arthritis Detection Using Clinical Parameters},
  howpublished = {EasyChair Preprint no. 12299},

  year = {EasyChair, 2024}}