Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction
V. Gokula Krishnan, M. V. Vijaya Saradhi, S. Sai Kumar, G. Dhanalakshmi, P. Pushpa, V. Vijayaraja
Abstract
The prevalence of cardiovascular diseases (CVD) makes it one of the leading reasons of death worldwide. Reduced mortality rates may result from early detection of CVDs and their potential prevention or amelioration. Machine learning models are a promising method for identifying risk variables. In order to make accurate predictions about cardiovascular illness, we would like to develop a model that makes use of transfer learning. Our proposed model relies on accurate training data, which was generated by careful Data Collecting, Data Pre-processing, and Data Transformation procedures.
Topics & Concepts
Feature selectionComputer scienceMachine learningArtificial intelligenceSelection (genetic algorithm)Model selectionFeature (linguistics)DiseaseTransfer of learningData miningMedicineLinguisticsPathologyPhilosophyArtificial Intelligence in HealthcareMachine Learning in Healthcare