Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
subject: Autoimmune diseases; K-means; PSO; Silhouette coefficient; Type 1 diabetes
description: An estimated 50 million Americans suffer from autoimmune diseases, as per the report from AARDA (American Autoimmune Related Diseases Association). More than 30 million people suffer in India from type 1 diabetes. More than $100 billion is spent on healthcare for autoimmune diseases in America, more than for cancer healthcare. Host genes and environmental factors control autoimmune diseases, and typically they do not have any specific cure. This paper proposes an artificial intelligence-based framework for the initial prediction of autoimmune diseases. This work attempts to identify characteristics of autoimmune diseases, and it lists the commonly occurring autoimmune diseases, the organs attacked by them, and the different stages involved. It also seeks to identify ways to prioritize the severity of the patient's disease, for providing treatments based on the severity, with the goal of reducing the pressure on the healthcare sector. Type 1 diabetes is an autoimmune disease and identifying the risk associated with diabetes and other related health problems could help to improve health worldwide. This work proposes a framework while exploring autoimmune disease prediction using machine learning techniques. The autoimmune disease considered is type 1 diabetes. The usage of machine learning techniques can help to enhance patient care and early prediction. This research is an attempt to explore the possibilities and also to propose a framework for early prediction of type 1 diabetes. Clustering is performed using K-means and PSO K-means. Validation of the clusters is carried out using silhouette coefficient. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
creator: Goswami M.
source: Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications, pp. 159-186.
publisher: Elsevier
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 978-044315533-8; 978-044315532-1
format: Online
language: English
type: Book chapter
coverage: Goswami M., CHRIST (Deemed to be University), Bangalore, India