vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff56d62b000000fec3060001000200 Heart disease is the foremost significant causes of transience within the world nowadays. It is a vital challenge to predict the cardiovascular disease in the range of clinical data investigation. Machine Learning (ML) is the most popular and powerful approach that has been appeared to be effective in making decisions and predictions from the huge amount of information delivered by the healthcare industry. ML techniques are also used in recent developments in wide areas of the Internet of Things (IoT). There are various studies done to predict the heart disease with ML techniques and it gives only a glimpse of it. In this paper, a simple TensorFlow model is proposed to find out major features by applying ML techniques that result in better accuracy in the prediction of cardiovascular disease. The prediction model is presented with diverse combinations of features and known classification algorithms. This version for coronary heart disorder with the ML based TensorFlow Model produces a more desirable overall performance with a higher accuracy stage in prediction.
D Komalavalli, R Sangeethapriya, R Indhu, N Kanimozhi, G Kasthuri
Sona College of Technology, India
Machine learning, heart disease prediction, feature selection, binary classification, TensorFlow
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 11 , Issue: 3 , Pages: 2323-2337 )
Date of Publication :
April 2021
Page Views :
197
Full Text Views :
2
|