An Efficient Disease Classification from Imbalanced Healthcare Data Using Ensemble Classifier with Oversampling Technique

Resumen

Recent advances in computing and developments in technology have facilitated the routine
collection and storage of medical data that can be used to support medical decisions.
However, in most countries, there is a first need for collecting and organizing patient’s data
in digitized form. Then, the collected data are to be analyzed in order for a medical decision
to be drawn, whether this involves diagnosis, prediction and course of treatment. This
research developed an ensemble classification model with benchmark dataset for Heart
disease diagnosis. The correct diagnosis performance of the automatic diagnosis system is
estimated by using classification accuracy, sensitivity and specificity analysis. The study
shows that, the boosting models have better choice for medical disease diagnosis application.