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A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method

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dc.contributor.author Ali, Md. Jubier
dc.contributor.author Das, Badhan Chandra
dc.contributor.author Saha, Suman
dc.contributor.author Biswas, Al Amin
dc.contributor.author Chakraborty, Partha
dc.date.accessioned 2024-03-21T05:42:22Z
dc.date.available 2024-03-21T05:42:22Z
dc.date.issued 2022-08-22
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/11760
dc.description.abstract Heart disease is considered one of the calamitous diseases which eventually leads to the death of a human, if not diagnosed earlier. Manually, detecting heart disease needs doing several tests. By analyzing the result of tests, it can be assured whether the patient got heart disease or not. It is time consuming and costly to predict heart disease in this conventional way. This paper describes different machine learning (ML) algorithms to predict heart disease incorporating a Cardiovascular Disease dataset. Although many studies have been conducted in this field, the performance of prediction still needs to be improved. In this paper, we have focused to find the best features of the dataset by feature selection method and applied six machine learning algorithms to the dataset in three steps. Among these ML algorithms, the random forest algorithm gives the highest accuracy which is 72.59%, with our best possible feature setup. The proposed system will help the medical sector to predict heart disease more accurately and quickly. en_US
dc.language.iso en_US en_US
dc.subject Machine learning en_US
dc.subject Cardiovascular disease en_US
dc.subject Heart disease en_US
dc.subject Treatment en_US
dc.title A Comparative Study of Machine Learning Algorithms to Detect Cardiovascular Disease with Feature Selection Method en_US
dc.type Article en_US


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