Analysis and study of mitral valve classification for diverse datasets using attention based machine learning classifiers

This study proposed an Attention Algorithm Based-Random Forest model (AAB-RF) To enhance the precision of the ML model, dataset 1 focuses on cardiac disease, including metrics like age, cholesterol, blood pressure, and other cardiovascular factors. The data set 2 aims at the mitral valve issues, especially focused on detecting the prosthetic valve anomalies. The two datasets are analyzed using machine learning classifiers, including K-Nearest Neighbors, Random Forest, Naive Bayes, Logistic Regression and advanced methods like Convolutional Neural Networks and a VGG-based framework. When analyzing the data set 1, the proposed AAB-RF model achieves the classification accuracy of 93% performs better than the other models like Naive Bayes of 88.52% and Support Vector Machine of 89% accuracy. Likewise, for data set 2, the proposed AAB-RF model reached the remarkable accuracy of 99.40% performs superior than CNN and VGG achieved an accuracy of 97% and 84% respectively whereas closely matching with the Vafaeezadeh et al. (2021) model's accuracy of 99.00%. The major advantage of integrating attention mechanism with the Random forest model enhances the feature selection and decision making, especially in datasets having the sophisticated interdependent nature. This study showcasing the AAB-RF model's efficiency in managing the multiple datasets which enables the robust and effective outcomes. The research outcomes shows its effectiveness which guides the physicians in diagnosing the heart disease and interpreting the mitral valve features with high accuracy and reliability.