A multi-machine learning model optimized with a genetic algorithm and Intel oneAPI achieved a 92.34% accuracy rate for the early diagnosis of heart disease using linear discriminant analysis.
Does a multi-machine learning model optimized with Intel oneAPI and a genetic algorithm improve the accuracy of early diagnosis of heart illness?
A machine learning approach using Intel oneAPI and genetic algorithms achieved 92.34% accuracy in detecting heart disease.
Heart failure is a frequent illness that might result in circumstances that could be fatal. Early heart illness identification is essential for prompt treatment and better patient outcomes. In this study, we offer a multi-machine learning model, trained with Intel oneAPI, technique for the early diagnosis of heart illness. To choose the most pertinent patient characteristics that will be used as input for our machine learning models, we employ a genetic algorithm (GA). To forecast the risk of cardiac disease, we optimise the models using a genetic algorithm and Intel oneAPI. Our findings demonstrate the high accuracy of our approach, with linear discriminant analysis optimised with GA producing the most accurate model with a 92.34% accuracy rate.
Ramakrishnan et al. (Thu,) conducted a other in Heart failure / cardiovascular disease. Multi-machine learning model optimized with genetic algorithm and Intel oneAPI was evaluated on Accuracy of early diagnosis of heart illness. A multi-machine learning model optimized with a genetic algorithm and Intel oneAPI achieved a 92.34% accuracy rate for the early diagnosis of heart disease using linear discriminant analysis.