HIDDEN PATTERN IDENTIFICATION USING SPATIAL CLUSTERING IN CENTRALIZED HEALTH CARE ARCHITECTURE FOR ARRHYTHMIA DETECTION

Abstract

The thesis aims to develop a way of finding out the trends that might exist between different cardiovascular diseases(CVD) and the socioeconomic, demographic and medical history of a patient, thus enabling the health sector to prevent large number of deaths caused by such diseases each year. To achieve this goal the current structure of healthcare sector needs to be somewhat changed and hence this thesis also suggests a new centralized healthcare structure. The thesis not only suggests ways of centralizing the healthcare structure of the country but also shows the ways in which it can be used in reducing CVD. A large portion of the paper deals with reading the acquired ECG of a patient the way Minnesota ECG Code elaborates and then using this data to find the possible arrhythmia the patient might be suffering from. After gathering this information data mining algorithms are used to find the possible trends between these diseases and other aspects of patients life. By identification of these patterns possible preventive measures and reorientation of health care facilities are suggested for the betterment of society.