Intelligent Hybrid Model for Predicting and Monitoring Heart Disease

Abstract

When it comes to the most important and critical aspect in Health care industry, so procedure of Heart Disease Diagnosis can be more conceptualised as this is only way by which we come to know about heart disease patients life – for such kind a risk factor helps patient department downfall disease at an stage. However, this process also causes errors in many cases and abounds with unexpected consequences or a patient dies by it. Therefore, the main difficult predictions in medical sector for heart disease diagnosis by doctors. So, in this technical generation we can very easily appreciate and value the role of artificial intelligence in healthcare. Accordingly, the essence of this study was to introduce a heart disease monitoring model that applies neuro fuzzy as hybrid ML methodology. In the proposed intelligent hybrid inference system, there are input variables used to diagnose disease at different stages. The system produces the output that yields the three different disease stages or levels. Thus, on this generated result base professional doctors of the heart can diagnose over patient and even decide better operation for treatment according to disease state. The k-fold cross validation technique does the same partitioning of the dataset and for testing. It also estimates the performance of system and by its results it accurately predicts stage of heart disease from which a patient is suffering with accuracy 98.90%.