4x-expert systems for early prediction of osteoporosis using multi-model algorithms
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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 4x-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 4x-expert system covers the extensive prediction and accuracy of any suspected bone disorder patients, ranging from 75% to 97%.
Açıklama
Anahtar Kelimeler
Deep Learning, Multi-Model, Decision Tree, Random Forest, Logistic Regression, Bone-Mineral Density, Diagnosis, Identification
Kaynak
Measurement
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
180
