Early Stage Prediction of Heart Disease Features using AdaBoost Ensemble Algorithm and Tree Algorithms
Keywords:
Random Forest feature selection methods, heart disease dataset, AdaBoost, Decision Tree, Multilayer Perception and Decision Tree.Abstract
Experts in diagnostics find it difficult to control the impact of risk factors since heart disease is a highly hazardous condition. Understanding cardiac disease is crucial to increasing forecast accuracy. This work presents experimental assessments carried out to evaluate the performance of models constructed with the help of classification algorithms and pertinent characteristics chosen by Random Forest Tree feature selection processes. Heart disease is the root cause of many illnesses worldwide. Many classification techniques were used in the analysis of medical data sets and diagnostic problems, such as heart disease. These techniques, nevertheless, were limited to tiny, balanced datasets; as a result, the characteristics had to be developed by trial and error. Furthermore, feature selection strategies have been heavily utilized by a number of sectors to improve classification performance. The purpose of this study is to present a complete strategy to improve the prediction of heart illness utilizing a variety of machine learning techniques, including Random Forest feature selection and AdaBoost, Decision Tree and Multilayer Perception. The outcomes of the trial shown improvements in prediction. AdaBoost scored 98.57, 73.08, 67.09, 69.09 and 80.55 in terms of accuracy, precision, recall, F1-score, and roc in the training model on an 80% data sample. In the experiment, we examined each classifier method on a 20% sample of data, and we found that the AdaBoost classifier model performed better in terms of accuracy, precision, recall, F1-score, and ROC, scoring 94.51, 48.33, 39.52, 41.78 and 66.71 respectively.
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