Heart disease prediction using machine learning algorithms
Day by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is m...
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| Veröffentlicht in: | IOP conference series. Materials Science and Engineering Jg. 1022; H. 1; S. 12072 - 12081 |
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01.01.2021
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| ISSN: | 1757-8981, 1757-899X |
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| Abstract | Day by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format. |
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| AbstractList | Day by day the cases of heart diseases are increasing at a rapid rate and it's very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease It is implemented on the.pynb format. |
| Author | Jindal, Harshit Khera, Rishabh Jain, Rachna Agrawal, Sarthak Nagrath, Preeti |
| Author_xml | – sequence: 1 givenname: Harshit surname: Jindal fullname: Jindal, Harshit email: harshit.jindal50@gmail.com organization: Student, Dept. Of Electronics And Communication Eng. Bharti Vidyapeeth's College Of Engineering – sequence: 2 givenname: Sarthak surname: Agrawal fullname: Agrawal, Sarthak organization: Student, Dept. Of Electronics And Communication Eng. Bharti Vidyapeeth's College Of Engineering – sequence: 3 givenname: Rishabh surname: Khera fullname: Khera, Rishabh organization: Student, Dept. Of Electronics And Communication Eng. Bharti Vidyapeeth's College Of Engineering – sequence: 4 givenname: Rachna surname: Jain fullname: Jain, Rachna organization: Faculty, Dept. Of Computer Science & Engineering Bharti Vidyapeeth's College Of Engineering – sequence: 5 givenname: Preeti surname: Nagrath fullname: Nagrath, Preeti organization: Faculty, Dept. Of Computer Science & Engineering Bharti Vidyapeeth's College Of Engineering |
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| SubjectTerms | Algorithms Cardiovascular disease Classifiers Health services Heart Heart diseases Machine learning Scientific papers Statistical analysis |
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