A Clinical Heart Disease Decision Supportive Optimized Mining Method for Effective Disease Diagnosis

Healthcare industry collects enormous amounts of healthcare data and required to mine and ascertain hidden information for constructive decision making. In recent years, the computer technology and machine learning methods with data mining approach increase techniques in assisting the doctors for productive decision making related to heart disease and stroke identification at an early stage. The needs to reduce the pattern matching loss by performing pattern matching while effectively improving the heart disease identification at much early stage poses severe challenges to the database community. In this paper a method called Clinical Heart Disease-Decision Supportive Optimized Mining (CHD-DSOM) is presented to overcome the pattern matching loss, and perform perfect pattern matching. The method CHD-DSOM categories to enable decision support with multi-dimensional analysis using Mahalanobis distance measure for obtaining dynamic data table information and typically built to support early stage of stroke identification and levels of heart disease. This helps in identifying the solution for different patterns and therefore reducing the