TY - GEN
T1 - Heart Disease Prediction Using Machine Learning
AU - Ghazal, Taher M.
AU - Ibrahim, Amer
AU - Akram, Ali Sheraz
AU - Qaisar, Zahid Hussain
AU - Munir, Sundus
AU - Islam, Shanza
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cardiovascular disease (CVD)
KW - Machine Learning (ML)
KW - Support Vector Machine (SVM)
UR - https://www.scopus.com/pages/publications/85160742243
UR - https://www.scopus.com/pages/publications/85160742243#tab=citedBy
U2 - 10.1109/ICBATS57792.2023.10111368
DO - 10.1109/ICBATS57792.2023.10111368
M3 - Conference contribution
AN - SCOPUS:85160742243
T3 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
BT - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023
Y2 - 7 March 2023 through 8 March 2023
ER -