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DC Field | Value | Language |
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dc.contributor.author | Achary, Rathnakar | - |
dc.contributor.author | R, Rohan | - |
dc.contributor.author | V, Pavan | - |
dc.contributor.author | B.C, Vivek | - |
dc.contributor.author | R, Shekhar | - |
dc.date.accessioned | 2024-02-01T04:34:29Z | - |
dc.date.available | 2024-02-01T04:34:29Z | - |
dc.date.issued | 2023 | - |
dc.identifier.isbn | 9798350300826 | - |
dc.identifier.uri | https://doi.org/10.1109/NMITCON58196.2023.10276230 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/5558 | - |
dc.description.abstract | The most important organ in the human body is the heart and it performs the function of blood circulation, blood pressure regulation, oxygenation, heart rate regulation, and cardiac output. It is essential that the patients can take care of their heart through a healthy lifestyle. Data analytics is one of the prominent methods that help to detect malfunctions of the heart. Predictive analytics play a vital role in the detection of heartrelated diseases by analyzing large amounts of patient data obtained from ECG, and MRI to categorize the patterns related to heart disease for patients who are at high risk of heartrelated problems. The intelligent method proposed in this research will help to anticipate the chance of developing heart disease in the future based on the current data such as medical history, symptoms, and lab results. In this paper, a statistical method is used to examine such a data set to analyze the risk of cardiac problems before it occurs and analyze the precision of the models used. © 2023 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023 | en_US |
dc.subject | Heart Disease Prediction | en_US |
dc.subject | Logistic Regression | en_US |
dc.subject | Knn | en_US |
dc.subject | Pca | en_US |
dc.subject | Svm | en_US |
dc.title | Predicting the Likelihood of Heart Disease Using Cognitive Analytics | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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