| dc.creator |
KÖSE, Utku |
|
| dc.creator |
Kottursamy, Kottilingam |
|
| dc.creator |
Prakash, U. |
|
| dc.creator |
Bui Thanh Hung, Bui Thanh Hung |
|
| dc.creator |
CENGİZ, KORHAN |
|
| dc.date |
2021-08-01T00:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:16:18Z |
|
| dc.date.available |
2021-12-03T11:16:18Z |
|
| dc.identifier |
22a5e7a9-01e0-4adc-9138-46d2de785a92 |
|
| dc.identifier |
10.1016/j.measurement.2021.109543 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/22a5e7a9-01e0-4adc-9138-46d2de785a92/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/90412 |
|
| dc.description |
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%. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
4x-expert systems for early prediction of osteoporosis using multi-model algorithms |
|
| dc.type |
info:eu-repo/semantics/article |
|