%0 Journal Article %1 noauthororeditor %A Harish, CH Sai %A Vamsi, G Krishna %A Akhil, G J P %A Sravan, J N V Hari %A Chowdary, V Mounika %D 2021 %E Kumar, Dr. Shiv %J International Journal of Preventive Medicine and Health (IJPMH) %K %N 2 %P 1-4 %R 10.35940/ijpmh.B1002.051221 %T Prediction of Heart Stroke using A Novel Framework – PySpark %U https://www.ijpmh.latticescipub.com/wp-content/uploads/papers/v1i2/B1002031221.pdf %V 1 %X Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.

@article{noauthororeditor, abstract = {Heart diseases are one of the most challenging problems faced by the Health Care sectors all over the world. These diseases are very basic now a days. With the expanding count of deaths because of heart illnesses, the necessity to build up a system to foresee heart ailments precisely. The work in this paper focuses on finding the best Machine Learning algorithm for identification of heart diseases. Our study compares the precision of three well known classification algorithms, Decision Tree and Naïve Bayes, Random Forest for the prediction of heart disease by making the use of dataset provided by Kaggle. We utilized various characteristics which relate with this heart diseases well, to find the better algorithm for prediction. The result of this study indicates that the Random Forest algorithm is the most efficient algorithm for prediction of heart disease with accuracy score of 97.17%.}, added-at = {2021-08-20T07:12:31.000+0200}, author = {Harish, CH Sai and Vamsi, G Krishna and Akhil, G J P and Sravan, J N V Hari and Chowdary, V Mounika}, biburl = {https://www.bibsonomy.org/bibtex/2564507d7ad8cfbbfc8f8b67c2afb61c8/ijpmh_lsp}, doi = {10.35940/ijpmh.B1002.051221}, editor = {Kumar, Dr. Shiv}, interhash = {b862fc604d26dbd4958d3b87ac333bec}, intrahash = {564507d7ad8cfbbfc8f8b67c2afb61c8}, issn = {2582-7588}, journal = {International Journal of Preventive Medicine and Health (IJPMH)}, keywords = {}, language = {En}, month = may, number = 2, pages = {1-4}, timestamp = {2021-08-20T07:12:31.000+0200}, title = {Prediction of Heart Stroke using A Novel Framework – PySpark}, url = {https://www.ijpmh.latticescipub.com/wp-content/uploads/papers/v1i2/B1002031221.pdf}, volume = 1, year = 2021 }