| _version_ | 1866901101137100800 |
|---|---|
| author | Tejaswini Zope Dr. K. Rajeswari Sushma Vispute |
| author_facet | Tejaswini Zope Dr. K. Rajeswari Sushma Vispute |
| contents | In this work, heart disease is regarded as one of the main causes in the world today. Doctors cannot easily predict it because it is a difficult task that requires experience and more predictive knowledge. There is a lot of knowledge available in the healthcare system on the web. However, there is a lack of effective analysis tools to capture the patterns and relationships hidden in the data. The automatic diagnosis system will improve medical efficiency and will reduce costs. The aims to predict the occurrence of disease-supporting data collected from medical research, especially in heart disease. The goal is to apply data processing technology to extract hidden patterns on the data set. These patterns are known for heart disease, and to predict whether patients have heart disease. The existence of these patterns is scored according to the scale. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18538566 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Application of Different Machine Learning Techniques for Predicting Heart Dieses Tejaswini Zope Dr. K. Rajeswari Sushma Vispute SVM Navie bayes Decision tree Random Forest Logistic regression In this work, heart disease is regarded as one of the main causes in the world today. Doctors cannot easily predict it because it is a difficult task that requires experience and more predictive knowledge. There is a lot of knowledge available in the healthcare system on the web. However, there is a lack of effective analysis tools to capture the patterns and relationships hidden in the data. The automatic diagnosis system will improve medical efficiency and will reduce costs. The aims to predict the occurrence of disease-supporting data collected from medical research, especially in heart disease. The goal is to apply data processing technology to extract hidden patterns on the data set. These patterns are known for heart disease, and to predict whether patients have heart disease. The existence of these patterns is scored according to the scale. |
| title | Application of Different Machine Learning Techniques for Predicting Heart Dieses |
| topic | SVM Navie bayes Decision tree Random Forest Logistic regression |
| url | https://doi.org/10.5281/zenodo.18538566 |