Approaches for Heart Disease Detection by Using Hybridisation in Machine Learning

EasyChair Preprint 4092

Abstract

Cardiac disorder treatment is the extremely challenging and inefficient operation to perform as it is known to be one of the most deadly diseases around the world today. Hospitals and healthcare centers are working tremendously hard to reduce the statistical growth of death due to cardiac disorder. The major cause of cardiac disorder is present lifestyle in which human beings are modified. The researchers and medical healthcare uses figures and facts of the patient which is suffering with this. Due to the inception of machine learning the task became much easier for them. As machine learning gives an easy platform. For anticipating cardiac illness without human impinging. This system will provide a prediction that will facilitate the researcher and the medical experts to diagnosis the disease is much easier stage and in time. In this paper we are dealing with the techniques of hybridization of machine learning which will provide high accuracy. Hybridisation deals with the combination of two or more techniques of machine learning so that the accuracy should be high in predicting heart disease. The main aim of this paper is to calculate various research works done on heart disease prediction dealing with the hybridization of machine learning. These methods introduced a new pathway for predicting heart disease.

Keyphrases: Genetic algorithm with Neuro-Fuzzy, Random Forest, classification algorithm, hybrid machine learning, linear method

@booklet{EasyChair:4092,
  author    = {Arfa Khan and P. Vigneshwaran},
  title     = {Approaches for Heart Disease Detection by Using Hybridisation in Machine Learning},
  howpublished = {EasyChair Preprint 4092},
  year      = {EasyChair, 2020}}