Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques
Cardiac disease prediction and detection are among the most difficult and important jobs encountered by medical practitioners. Heart disease can be caused by a range of factors, including a sedentary lifestyle, stress, alcohol, cigarette intake, and so on. The current prediction algorithms focus on...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/3094 https://doi.org/10.1109/ICSPIS57063.2022.10002692. |
| الوسوم: |
إضافة وسم
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| _version_ | 1862980611235381248 |
|---|---|
| author | Tyagi, Sapna |
| author2 | Sirohi, Preeti Maheshwari, Piyush |
| author2_role | author author |
| author_facet | Tyagi, Sapna Sirohi, Preeti Maheshwari, Piyush |
| author_role | author |
| dc.creator.none.fl_str_mv | Tyagi, Sapna Sirohi, Preeti Maheshwari, Piyush |
| dc.date.none.fl_str_mv | 2022 2025-05-22T10:45:37Z 2025-05-22T10:45:37Z |
| dc.identifier.none.fl_str_mv | Tyagi, S. et al. (2022) “Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques,” in 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pp. 107–112. 2831-3844 https://bspace.buid.ac.ae/handle/1234/3094 https://doi.org/10.1109/ICSPIS57063.2022.10002692. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | 2022 5th International Conference on Signal Processing and Information Security (ICSPIS)107-112 |
| dc.subject.none.fl_str_mv | prediction , healthcare , regression , forecasting |
| dc.title.none.fl_str_mv | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| dc.type.none.fl_str_mv | Article |
| description | Cardiac disease prediction and detection are among the most difficult and important jobs encountered by medical practitioners. Heart disease can be caused by a range of factors, including a sedentary lifestyle, stress, alcohol, cigarette intake, and so on. The current prediction algorithms focus on forecasting the illness label though the likelihood of getting the condition is still unknown. This study is conducted to forecast the heart disease progression well in advance so that essential action can be taken before the condition becomes severe. As a result, the research proposes a model for predicting the likelihood of heart disease incidence using logistic regression capabilities. |
| id | budr_524db360831a4724f6af86b7f8a4e456 |
| identifier_str_mv | Tyagi, S. et al. (2022) “Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques,” in 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pp. 107–112. 2831-3844 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/3094 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering TechniquesTyagi, SapnaSirohi, PreetiMaheshwari, Piyushprediction , healthcare , regression , forecastingCardiac disease prediction and detection are among the most difficult and important jobs encountered by medical practitioners. Heart disease can be caused by a range of factors, including a sedentary lifestyle, stress, alcohol, cigarette intake, and so on. The current prediction algorithms focus on forecasting the illness label though the likelihood of getting the condition is still unknown. This study is conducted to forecast the heart disease progression well in advance so that essential action can be taken before the condition becomes severe. As a result, the research proposes a model for predicting the likelihood of heart disease incidence using logistic regression capabilities.IEEE2025-05-22T10:45:37Z2025-05-22T10:45:37Z2022ArticleTyagi, S. et al. (2022) “Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques,” in 2022 5th International Conference on Signal Processing and Information Security (ICSPIS), pp. 107–112.2831-3844https://bspace.buid.ac.ae/handle/1234/3094https://doi.org/10.1109/ICSPIS57063.2022.10002692.en2022 5th International Conference on Signal Processing and Information Security (ICSPIS)107-112oai:bspace.buid.ac.ae:1234/30942025-05-22T10:48:00Z |
| spellingShingle | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques Tyagi, Sapna prediction , healthcare , regression , forecasting |
| title | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| title_full | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| title_fullStr | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| title_full_unstemmed | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| title_short | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| title_sort | Predicting Cardiovascular Disease in Patients with Machine Learning and Feature Engineering Techniques |
| topic | prediction , healthcare , regression , forecasting |
| url | https://bspace.buid.ac.ae/handle/1234/3094 https://doi.org/10.1109/ICSPIS57063.2022.10002692. |