Heart disease diagnosis process using MRI segmentation and lasso net classification ML

Abstract

In this work, heart disease prediction and classification mechanism is proposed based on MRI local segmentation and Lasso net classification (LSLN) machine learning technique. In the brain MRI scan model, segmentation is generally utilized measuring technique. The heart visualization of the anatomical segment can give the tumour or disease information. After the pre-processing stage image has been processed for classification state, in this lasso net regression model is used for regression and classifier. The leadingimportant of this investigation work remains to find out the heart disease diagnosis and classify the disease. The performance metrics have calculated at final such as PSNR, efficiency, throughput, F1-score as 96.12%, 98.74%, 97.25%, and 97.48% respectively. The outcomes which are obtained have more improvement, and these are competing with current technology.