Machine learning model matters its accuracy: a comparative study of ensemble learning and AutoML using heart disease prediction

Keyphrases

• Machine Learning Models 100%
• Heart Disease Prediction 100%
• Ensemble Learning 100%
• Automatic Machine Learning 100%
• Prediction Accuracy 60%
• Logistic Regression 40%
• K-nearest Neighbor (K-NN) 40%
• Accuracy Score 40%
• Multi-layer Perception 40%
• Model Accuracy 40%
• Neighborhood Perceptions 40%
• Decision Tree 20%
• Multiple Models 20%
• Artificial Neural Network 20%
• Learning Support 20%
• Quality Model 20%
• Best Model 20%
• Generalized Linear Model 20%
• Regression Problem 20%
• Strong Model 20%
• New Researchers 20%
• Regression Network 20%
• Neural Network Model 20%
• Precision Accuracy 20%
• Accurate Classification 20%
• Random Forest 20%
• Model Prediction 20%
• Support Vector 20%
• Binary Classification 20%
• Feature Engineering 20%
• Random Forest Regression 20%
• Tabular Form 20%
• Regression Support Vector Machine 20%
• Gradient Boosting Model 20%
• R2 Score 20%
• Ensemble Machine Learning 20%
• Weak Learner 20%
• Extra Trees Model 20%
• Perception Algorithm 20%
• Heart Disease Classification 20%
• Binary Regression 20%
• Heart Disease Dataset 20%
• Best Mode 20%
• Overall Modeling 20%
• Distributed Random Forest 20%

Computer Science

• Ensemble Learning 100%
• Machine Learning 100%
• Learning System 100%
• Random Decision Forest 22%
• Model Accuracy 22%
• Logistic Regression 22%
• Regression Problem 11%
• Neural Network Model 11%
• Prediction Accuracy 11%
• Support Vector 11%
• Individual Model 11%
• Open Source 11%
• Decision Trees 11%
• quality model 11%
• Gradient Boosting 11%
• Classification Problem 11%
• Support Vector Machine 11%
• Model Prediction 11%
• Binary Classification 11%
• Artificial Neural Network 11%