Abstract
In this study, we evaluated the ability of a linear regression algorithm to predict heart disease risk in individuals. We use data from trusted sources and perform the necessary preprocessing to clean and provide the data for the model. The results of the analysis show that the linear regression algorithm can be used well to predict the risk of heart disease in individuals with a fairly high degree of accuracy. We also evaluated several factors that influence heart disease risk and demonstrated that they could be identified and integrated into our model to improve its performance. In addition, we also evaluated the validation methods used to evaluate our models and demonstrated that they can be used to objectively determine model performance. The results from this study provide a solid foundation for developing a better heart disease prediction system in the future. And the results of this study are quite accurate enough to give good results with a Root Mean Squared Error: 0.379 +/- 0.000 and Squared Error: 0.144 +/- 0.229
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
