Heart Failure Prediction with Ensembled Learning

Cardiovascular disease is becoming an increasingly problematic world today. Sudden arrest of Aldous can lead to serious illnesses such as brain damage, nervous system disorders and even death. This makes heart failure disorder to be predicted on an early stage rather than repenting later[8]. A proposed decision support system based on machine learning helps physicians efficiently diagnose patients with heart disease. However, these diseases can be predicted using various machine learning models. Performance is evaluated using logistic regression, K-nearest neighbor method, random forest, and ANN. The accuracy of the random forest algorithm is 83.15%. This was far more accurate than the other algorithms described earlier. The proposed Ensemble learning is used to improve where more classification algorithms can be used simultaneously on a single dataset. The accuracy of the proposed model is 86.41 %. The proposed model helps in predicting the heart disease of various people with various complications.