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Nomogram based on multivariate logistic regression-based for probability prediction of DSPN severity. Nomogram for predicting DSPN severity was formed utilizing seven different predictors, namely 10-gm filament, Vibration perception (L), Vibration perception (R), Appearance of Callus, Appearance of Deformities, Previous Diabetic Neuropathy, Appearance of Fissure.

Journal: Diagnostics

Article Title: A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument

doi: 10.3390/diagnostics13020264

Figure Lengend Snippet: Nomogram based on multivariate logistic regression-based for probability prediction of DSPN severity. Nomogram for predicting DSPN severity was formed utilizing seven different predictors, namely 10-gm filament, Vibration perception (L), Vibration perception (R), Appearance of Callus, Appearance of Deformities, Previous Diabetic Neuropathy, Appearance of Fissure.

Article Snippet: A diagnostic nomogram was constructed by Zlotnik and Abraira [ ] using multivariate logistic regression analysis in Stata/MP software (StataCorp LLC, College Station, TX, USA).

Techniques: