A Data-Driven Ensemble Learning Model For Heart Disease Prediction Using Feature Representation And Classification
Keywords:
heart disease, prediction, machine learning, feature representation, accuracyAbstract
Heart disease is one of the most common causes of death in the general population. The prognosis of patients with heart conditions is greatly impacted by early detection. Several recognized factors can contribute to life-threatening cardiac problems, such as Age, sex, heart rate, cholesterol, and sugar. However, an expert may find it challenging to assess each patient while considering these factors due to the large number of variables. The work suggest assessing patients' risk of cardiovascular disease by combining Machine Learning (ML) and Deep Learning (DL) with feature augmentation techniques to form an ensemble model. The DenseNet, Gated Network Model (GNM) and Multi-Layer Perceptron’s (MLP) are combined to form the ensemble model. The results of the proposed methods demonstrate a significant improvement, particularly for a condition that impacts a large population, surpassing previous methods by 4.4% and achieving a 95.89% accuracy rate
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