Heart Disease Prediction Using Machine Learning

• Published in: 2023 International Conference on Business Analytics for Technology and Security (ICBATS) pp. 1 - 6
• Main Authors: Ghazal, Taher M., Ibrahim, Amer, Akram, Ali Sheraz, Qaisar, Zahid Hussain, Munir, Sundus, Islam, Shanza
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.03.2023
• Subjects: Artificial intelligence; Cardiovascular disease (CVD); Classification algorithms; Heart; Machine learning; Machine Learning (ML); Machine learning algorithms; Prediction algorithms; Security; Support Vector Machine (SVM); Support vector machines
• DOI: 10.1109/ICBATS57792.2023.10111368

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.