Health Consumer Usage Patterns in Management of CVD using Data Mining Techniques

Abstract

The Healthcare system is exposed to the increasing impact of chronic diseases including cardiovascular diseases; it is of much importance to analyze and understand the health trajectories for efficient planning and fair allotment of resources. This work proposes an approach based on mining clinical data to support the exploration of health trajectories related to cardiovascular diseases. As the health data are highly confidential, we aimed to conduct our experiments using a large, synthetic, longitudinal dataset, constituted to represent the CVD risk factors distribution and temporal sequence of events related to heart failure hospitalization and readmission. This research work analyses and represents the temporal events or states of the patient's trajectory with the aim of understanding the patient's journey in the management of the chronic condition and its complications by using data mining techniques. This study focuses on developing an efficient algorithm to find cohesive clusters for handling the temporal events. Clustering health trajectories have been carried out by proposing an improved version of the Ant-based clustering algorithm. Insights from this study can potentially result in evidence that these approaches are useful in understanding and analyzing patient's health trajectories for better management of the chronic condition and its progression.