Adaptively Generated Association-Ruled Pre-pruned Boosted Decision Trees for Better Prediction on Diabetes

This research aims to comparatively investigate, evaluate and develop various prediction models by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest, Adaptively generated Association Ruled Pre-pruned or AgARPp AdaBoostM2, AgARPp Gradient Boosting, and AgARPp XGBoost <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to predict and classify the three different outcomes of diabetes, i.e. diabetes, pre-diabetes and non-diabetes. This is based on various factors such as demographic, clinical, lifestyle factors and attributes of individuals. The primary objective is to identify the most accurate prediction algorithm that can be used in the healthcare industry to assist healthcare professionals in the early and more accurate diagnosis of various stages of diabetes. The dataset used is the online diabetes dataset. Besides ANN, SVM and Random Forest, the rest of AdaBoostM2, Gradient Boosting and XGBoost are preprocessed using Adaptively generated Association Ruled Pre-pruning techniques. Results after applying the six prediction models were comprehensively analyzed, compared and studied. These prediction models were evaluated based on their respective evaluation metrics, mainly accuracy, precision, recall and F1-score. Among the results of all the six evaluated prediction models, AgARPp AdaBoostM2 showed the best prediction accuracy result of 98.06% when this model was being implemented.