| Abstrakt: |
Aim: The main objective of this study was to predict upper urinary tract damage utilizing novel approaches, such as machine learning models, by incorporating simple predictors alongside established radiological and clinical factors. Materials and Methods: In this retrospective study, a total of 191 patients who underwent blood tests, urine analysis, imaging, and urodynamic studies (UDS) in order to assess their nephrological and urological status were included. Basic statistical analyses were conducted using IBM SPSS Version 25. A significance level of p<0.05 was employed to establish statistical significance. The machine learning analyses were performed on Ddsv4-series Azure Virtual Machines, equipped with 32 vCPUs with a memory capacity of 128 GiB. Results: In the model where clinical and imaging data were jointly assessed, the k-nearest neighbor (KNN) model demonstrated the highest performance, achieving values of 0.813 area under the curve and 0.854 accuracy. For the KNN Model, the best predictors for kidney function loss were as follows: neutrophil/lymphocyte (1.0577), abnormal bladder in ultrasound (1.054), vesicoureteral reflux (0.901), ferritin (0.898), neutrophil/albumin (0.678), platelet/lymphocyte (0.619), increased detrusor leakage pressure (0.435), age (0.3505), decreased bladder capacity in urodynamics (0.3009), and white blood cell (0.266). Conclusion: Based on our findings, initial patient evaluation through basic blood and urine tests, ultrasonography, UDS, and voiding cystourethrography is crucial for identifying risk factors and preventing renal damage. Complete blood count-derived inflammatory biomarkers offer cost-effective and accessible alternatives to other radiological tools in primary care settings. These machine learning models may hold clinical relevance in pre-clinical or resource-limited hospitals, by guiding clinicians in implementing preventative measures. [ABSTRACT FROM AUTHOR] |
