Analysis and Mortality Prediction using Multiclass Classification for
  Older Adults with Type 2 Diabetes
Ruchika Desure Gutha Jaya Krishna
Designing proper treatment plans to manage diabetes requires health
practitioners to pay heed to the individuals remaining life along with the
comorbidities affecting them. Older adults with Type 2 Diabetes Mellitus (T2DM)
are prone to experience premature death or even hypoglycaemia. The structured
dataset utilized has 68 potential mortality predictors for 275,190 diabetic
U.S. military Veterans aged 65 years or older. A new target variable is
invented by combining the two original target variables. Outliers are handled
by discretizing the continuous variables. Categorical variables have been dummy
encoded. Class balancing is achieved by random under-sampling. A benchmark
regression model is built using Multinomial Logistic Regression with LASSO.
Chi-Squared and Information Gain are the filter-based feature selection
techniques utilized. Classifiers such as Multinomial Logistic Regression,
Random Forest, Extreme Gradient Boosting (XGBoost), and One-vs-Rest classifier
are employed to build various models. Contrary to expectations, all the models
have constantly underperformed. XGBoost has given the highest accuracy of 53.03
percent with Chi-Squared feature selection. All the models have consistently
shown an acceptable performance for Class 3 (remaining life is more than 10
years), significantly low for Class 1 (remaining life is up to 5 years), and
the worst for Class 2 (remaining life is more than 5 but up to 10 years).
Features analysis has deduced that almost all input variables are associated
with multiple target classes. The high dimensionality of the input data after
dummy encoding seems to have confused the models, leading to
misclassifications. The approach taken in this study is ineffective in
producing a high-performing predictive model but lays a foundation as this
problem has never been viewed from a multiclass classification perspective.