Heart Disease Prediction Using Machine Learning

  • Taher M. Ghazal
  • , Amer Ibrahim
  • , Ali Sheraz Akram
  • , Zahid Hussain Qaisar
  • , Sundus Munir
  • , Shanza Islam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The heart disease cases are rising day by day and it is very Important to predict such diseases before it causes more harm to human lives. The diagnosis of heart disease is such a complex task i.e., it should be performed very carefully. The work done in this research paper mainly focuses on which patients has more chance to suffer from this based on their various medical feature such as chest pain etc. We proposed a system of heart disease prediction that is used to diagnose whether the patient is a victim or not by using the previous medical features of the patient. Support vector machine and k-nearest neighbor algorithms of machine learning are used to predict and classify the patient with heart disease. The models gave satisfactory results and were capable for predicting a heart disease by using k-nearest neighbor and support vector machine which gave a good accuracy in contrast to the algorithms that were used in the previous research such as naive bayes etc.

Original languageEnglish
Title of host publication2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350335644
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 - Dubai, United Arab Emirates
Duration: Mar 7 2023Mar 8 2023

Publication series

Name2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

Conference

Conference2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Country/TerritoryUnited Arab Emirates
CityDubai
Period3/7/233/8/23

Keywords

  • Artificial intelligence
  • Cardiovascular disease (CVD)
  • Machine Learning (ML)
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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