Habtamu Geremew, Samuel Abdisa, Elyas Melaku Mazengia, Werkneh Melkie Tilahun, Aysheshim Belaineh Haimanot, Tigabu Kidie Tesfie, Anteneh Lamesgen Mneneh, Muluye Gebrie Mengie, Bekalu Endalew, Molla Yigzaw Birhanu, Lakew Asmare, Mulat Belay Simegn
Anxiety and depression among cancer patients in Ethiopia: a systematic review and meta-analysis
Frontiers in psychiatry
diabetic nephropathy dn major complication diabetes mellitus significantly impacts global health identifying individuals risk developing dn crucial early intervention improving patient outcomes study aims develop validate machine learningbased predictive model using integrated biomarkers crosssectional analysis conducted baseline dataset involving 2184 participants without dn categorized based development dn followup period 36 months dn n1270 nondn n914 various demographic clinical parameters analyzed findings validated using independent dataset comprising 468 participants 273 developing dn 195 remaining nondn followup period machine learning algorithms alongside traditional descriptive statistics logistic regression used statistical analyses elevated levels serum creatinine urea reduced egfr alongside increased prevalence retinopathy peripheral neuropathy prominently observed developed dn validation independent dataset confirmed models robustness consistency svm model demonstrated superior performance training set auc079 f1score074 testing set auc083 f1score082 outperforming models significant predictors dn included serum creatinine egfr presence diabetic retinopathy peripheral neuropathy integrating machine learning algorithms clinical biomarker data baseline offers promising avenue identifying individuals risk developing diabetic nephropathy type 2 diabetes patients 36month period