Graphical User Interface Based Heart Disease Analysis In Machine Learning Algorithms

Abstract

As we all know; heart disease affects the majority of individuals in the world today. About one person per minute in this age passed away from heart-related illnesses. As a result, heart disease-related mortality has recently become a serious problem. Roughly one person jumps into the pool every minute as a result of heart illness. We are all aware that enormous volumes of unethical data are produced in our healthcare systems. We need a system that is well-structured and user-friendly for handling this massive type of data. We can therefore introduce machine learning algorithms to the outline as a result of this. We have included a GUI-based machine-learning method for heart disease prediction in our future system. We have gathered the 14-field dataset for our system and pre-processed it utilizing data analysis algorithms. Then, using Python Jupiter, we compared with the fundamental classification methods such as SVM, DT, RF, and LR. Our analysis of this technique led us to the conclusion that logistic regression ensured a precision and warmth of 98% and 89%, correspondingly.