Objectives: To compare efficacy and diagnostic value of different biomarkers and demographic characteristics in the early diagnosis of serious bacterial infection (SBI) of infants younger than 90 days. To build a clinical prediction model with whom it will be possible to diagnose SBI with more accuracy than with independent biomarkers. Patients and Methods: Our patients were infants younger than 90 days presenting with fever without apparent source that were hospitalized between January 1st, 2014 and December 31st, 2015 with suspicion of having SBI. They were subsequently classified into serious bacterial infection group or other diseases group based on the final diagnosis. Research was accomplished by retrospective cross-sectional analysis of medical records of Department of Pediatrics at University Hospital Centre Split. Ethics Committee of the University Hospital Centre Split approved the study. Results: Out of 181 enrolled patients, SBI was confirmed in 70. The most common diagnosis was urinary tract infection (68.6%), followed by pneumonia (12.9%), sepsis (11.4%), gastroenterocolitis (5.7%) and meningitis (1.4%). Male gender was shown to be a risk factor for SBI in this population. White blood cell count, absolute neutrophil count (ANC) and C-reactive protein (CRP) were confirmed as the independent predictors of serious bacterial infection, with CRP as the best one according to diagnostic capabilities. Clinical prediction model for diagnosing SBI which included white blood cell count, CRP, highest measured fever and gender has shown sensitivity of 74.29% and specificity of 88.29%. Conclusions: CRP is a more superior biomarker in diagnostics of serious bacterial infection comparing to white blood cell count and ANC. Clinical prediction model was shown to be better in predicting SBI than independent biomarkers. Although it showed high sensitivity and specificity, its true strength should be determined using validation cohort.