01518nam a22001337a 4500082000800000100002200008245020300030264003800233300002600271500095600297650004201253700003201295856005701327  a610  aKazim, Assad Ali   aDevelopment of Machine Learning Model for Classification of No Specific Chronic Lower Back Patients from Healthy Patients Usin Spinal Kinematic Data Through Motion Capture (MOCAP) /cAssad Ali Kazim  aIslamabad : bSMME- NUST; c2025.  a70p.bSoft Copyc30cm  aNon-specific lower back pain remains a global physiological disability surpassing
pathological diseases. Its diagnosis through modern machines are expensive and frequent
doses of radiation lead to deterioration of body cells. Recently with development of allied
technologies such as Motion Capture (MoCap) can evaluate skeletal motion of the patient.
This technology vastly used in cinematography has a hidden usage for diagnosis of patients
with NSLBP. Through various sampling of motion and effective utilisation of Machine
Learning, we can classify a healthy patient from NSLBP patient. Various supervised
learning models such as Scalar Vector Machine (SVM), Random Forest (RF), XGBoost
and ANN have shown promising results which reflects that such AI tools can predict
patients having NSLBP. Effective utilization can lead to exact determination of location
where said problem is being developed in the patient through various motion examinations.  aMS Biomedical Engineering (BME)        aSupervisor : Dr. Asim Waris  uhttp://10.250.8.41:8080/xmlui/handle/123456789/57255