A proactive model to predict osteoporosis: An artificial immune system approach. Issue 4 (4th May 2021)

• Record Type: Journal Article
• Authors: Periasamy, Keerthika; Periasamy, Suresh; Velayutham, Sathiyamoorthi; Zhang, Zuopeng; Ahmed, Syed Thouheed; Jayapalan, Anitha
• Other Names: Chang Victor guestEditor.; Ramachandran Muthu guestEditor.; Li Chung‐Sheng guestEditor.; Zamorano Mariano Rincón guestEditor.; Tomás Rafael Martínez guestEditor.; Vicente José Manuel Ferrández guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-04
• Subjects: artificial immune system -- bio‐inspired -- bone mineral density -- classifiers -- convolutional neural network -- machine learning -- osteoporosis; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12708
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Osteoporosis disease is caused by hormonal changes, vitamin D, and calcium deficiency. With current technologies, the identification of osteoporosis requires many tests with the support of medications. Bone mineral density is a typical measure implemented using a DEXA scan which can be very costly. Such high technology equipment is usually not accessible for remote people, and thus a low‐cost screening system is very appealing. This article proposes an osteoporosis prediction system that effectively determines its possibility of occurrence based on essential factors such as smoking habits and calcium level so that the people at high risk can be referred to access the DEXA scanner. Our proposed system is implemented by an improved version of the artificial immune system, enabling care providers to take precautionary measures at the right time to avoid the early development of osteoporosis. The experiments demonstrated a promising result of 94% prediction accuracy that proved its usefulness in identifying people with potential osteoporosis in the future.