A Hybrid Technique to Early Predict Ischemic Heart Disease

Abstract

The creation of a model for the identification of Heart diseases using modest non-laboratory risk factors is critical in protective care, even for those of us with high risk. Model allows doctors/epidemiologists to diagnose a patient as taking cardiac diseases effectively. Its system provides for a fast initial diagnosis. The medical organization contains a huge amount of data that can be pre-processed with data mining techniques. Experiments show that the XGboost & Artificial Bee Colony (ABC) hybrid solution results in good results is more reliable, or improves precise detection of heart failure with only a reasonable accuracy of 98.40 percent over other algorithms referred to in this report.