title: Return to Work After Lumbar Microdiscectomy - Personalizing Approach Through Predictive Modeling.
abstract: Lumbar disc herniation (LDH) is the most common disease among working population requiring surgical intervention. This study aims to predict the return to work after operative treatment of LDH based on the observational study including 153 patients. The classification problem was approached using decision trees (DT), support vector machines (SVM) and multilayer perception (MLP) combined with RELIEF algorithm for feature selection. MLP provided best recall of 0.86 for the class of patients not returning to work, which combined with the selected features enables early identification and personalized targeted interventions towards subjects at risk of prolonged disability. The predictive modeling indicated at the most decisive risk factors in prolongation of work absence: psychosocial factors, mobility of the spine and structural changes of facet joints and professional factors including standing, sitting and microclimate.
authors.affiliation: Department for occupational health, Health center Novi Sad, Serbia.
authors.forename: Monika
authors.initials: M
authors.lastname: Papić
authors.affiliation: BioSense Institute, Novi Sad, Serbia.
authors.forename: Sanja
authors.initials: S
authors.lastname: Brdar
authors.affiliation: Medical Faculty, University of Novi Sad, Serbia.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Papić
authors.affiliation: Faculty of Technical Sciences, University of Novi Sad, Serbia.
authors.forename: Tatjana
authors.initials: T
authors.lastname: Lončar-Turukalo
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D017586:Diskectomy / Q000379:methods*; D005260:Female; D006801:Humans; D007405:Intervertebral Disc Displacement / Q000601:surgery*; D008297:Male; D008866:Microsurgery / Q000379:methods; D008962:Models, Theoretical; D009787:Occupational Medicine; D062707:Return to Work; D055771:Serbia; D060388:Support Vector Machine; D016896:Treatment Outcome