Design of Expert System for the Prediction of Acute Cardiac Arrest with Classification Techniques

Abstract

Acute Cardiac Arrest or Syndrome is one of the stimulating arenas of the medical realm that is complicated to be predicted earlier to its occurrence. The deficiency of oxygen supply will lead to improper electric balance of heart rhythms and will cause change in irregular heartbeats consistently over a period of time which on a day causes imbalance on all body functions resulting in sudden arrest of heart valves and blood flow. Since the response time of the disease is very less, it is required to predict it in early stage. The chief objective of the research is to develop an Expert System to predict the incidence of Acute Cardiac Arrest at current stage or in future for a patient based on Multi-variate Feature Predictors inclined with Machine Learning classification algorithms designed to handle independent and multi-class variables to predict the disease. The social need of the research is to develop a framework model with novel algorithmic techniques that would encourage medical practitioners to identify early prediction patterns for Acute Cardiac Arrest as to nurture solutions to save patients. A detailed literature study on various algorithms and their accuracy in predicting heart diseases is conducted among which 50 reviews are considered vital for designing the architecture of the Expert System design for prediction of Acute Cardiac Arrest. newline