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Algorithm for diabetes risk evaluation from past gestational diabetes data.

Gestational diabetes mellitus (GDM) is defined as glucose intolerance that is diagnosed in pregnancy period, leading to possible complications for both mother and fetus during pregnancy. The aim of this study was to build an objective method to evaluate diabetes mellitus (DM) risk from past GDM data recorded 15 years ago and find a short list of most informative indicators. The dataset consists of demographic, lifestyle, clinical, genetic and pregnancy related information recorded 15 years ago. Due to the large time gap data are limited and have missing values (MVs). Follow-up tests were performed to see if DM or impaired metabolism has developed after pregnancy with previously diagnosed GDM. The research steps involve pre-processing data to evaluate MVs, finding most informative attributes and testing standard classification algorithms to combine in to most effective voting meta-algorithm. Initially the attributes and records with large number of MVs were rejected. A small percentage (2.04%) was imputed using regression based methods. The data set was prepared for two scenarios: classification in two classes (1-healthy; 2-impaired metabolism including DM) and three classes (1-healthy; 2-impaired metabolism; 3-DM). Voting meta-algorithm combining best algorithms of 21 from five different groups including Bayesian, regression, lazy, rule, and decision trees makes classification more objective and not depending on preferences. Relative frequency of occurrence (RFO) analysis of attributes combined with voting meta-algorithm helped finding optimal amount of attributes giving best possible classification result. The algorithm applied to two class data set with 12 selected attributes produced accuracy of 75.85 and AUC = 0.82 with standard error of 0.11. Similarly for three class dataset the 9 attributes were selected allowing to reach classification accuracy 63.77 and AUC = 0.76 with standard error of 0.1. Meta-algorithm based classification of limited anamnestic GDM related data for DM prediction is proving to be effective. Testing multiple algorithms and performing RFO analysis appears to be natural and objective way of selecting most informative attributes and evaluating their importance.