4x-expert systems for early prediction of osteoporosis using multi-model algorithms. (August 2021)

• Record Type: Journal Article
• Authors: U M, Prakash; Kottursamy, Kottilingam; Cengiz, Korhan; Kose, Utku; Thanh Hung, Bui
• Is Part Of: Measurement. Volume 180(2021)
• Publication Date: 2021-08
• Subjects: Deep learning -- Multi-model -- Decision tree -- Random forest -- Logistic regression; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109543
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The proposed 4 x -expert system is used to create a prediction system for osteoporosis suspected patients. It is designed using multi model machine learning algorithms. Which uses and displays the results from all the systems along with performance metrics. Thus 4x-expert systems, helps in better diagnosing osteoporosis cases with reduced computational cost. Abstract: Osteoporosis occurs due to micro-architectural deterioration of the bone tissues with an increased risk of bone fragility, which can cause fractures in the bone without much pressure applied to it. The T-score of a person's bone density report can be used to calculate the difference between BMD to that of healthy bones. Currently, osteoporosis is detected using conventional methods like DXA scans or high computational power requiring FEA tests. Considering individual approaches and mono-prediction techniques leads to omission of micro-fractional prediction parameters. In this paper, we have proposed a 4 x -expert system for suspected osteoporosis patients, which is designed using multi model machine learning algorithms for improving prediction and accuracy through the various computational process. The experiment results shows, that the 4 x -expert system covers the extensive prediction and accuracy of any suspected bone disorder patients, ranging from 75% to 97%.