Development and Comparison of Three Data Models for Predicting Diabetes Mellitus Using Risk Factors in a Nigerian Population.

ABSTRACT:

Objective

This study developed and compared the performance of three widely used predictive models-logistic regression (LR), artificial neural network (ANN), and decision tree (DT)-to predict diabetes mellitus using the socio-demographic, lifestyle, and physical attributes of a population of Nigerians.

Methods

We developed three predictive models using 10 input variables. Data preprocessing steps included the removal of missing values and outliers, min-max normalization, and feature extraction using principal component analysis. Data training and validation were accomplished using 10-fold cross-validation. Accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC) we

SUBMITTER: Odukoya O 

PROVIDER: S-EPMC8850175 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature