Novel random forest in conjunction with Gaussian naive bayes for improvement in the accuracy of coronary artery heart disease classification

• Published in: AIP conference proceedings Vol. 2853; no. 1
• Main Authors: Sadwika, Uggumudi, Yuvaraj, R.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 07.05.2024
• Subjects: Algorithms; Datasets; Heart diseases
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0197604

This study aims to classify human Coronary Artery Heart Disease using the cutting-edge Random Forest Algorithm and to compare its performance to that of the more traditional Naive Bayes algorithm. Forty human subjects were used to create a dataset on kaggle (Framingham dataset). Naive Bayes (NB) is in Group 1, and Random Forest (RF) is in Group 2. (RF). The G power was determined using a 0.05 alpha and 80% confidence. The innovative Random Forest approach outperformed the Naive Bayes algorithm by a wide margin, with 84% accuracy and 78% precision, respectively. The significance levels for Novel Random Forest’s accuracy and precision were 0.025 and 0.038, respectively (p 0.05). The results of this study show that the Novel Random Forest algorithm outperforms the Naive Bayes algorithm by a wide margin.