التفاصيل البيبلوغرافية
| العنوان: |
Heart Attack Prediction using Machine Learning |
| المؤلفون: |
D.Swetha, V.Priyanka, V. Ganga Sushma, L.Bhuvaneshwar, Sivachandra Kagolla |
| المصدر: |
Research and Applications: Emerging Technologies, 6(3), 29-37, (2024-07-05) |
| بيانات النشر: |
Zenodo |
| سنة النشر: |
2024 |
| المجموعة: |
Zenodo |
| مصطلحات موضوعية: |
Heart attack prediction, Electronic health data, Machine learning algorithms, Support Vector Classifier |
| الوصف: |
Heart attack prediction is one in every of the real causes of horribleness inside the world’s populace. The clinical records evaluation includes a particularly important disorder i.e., cardiovascular disease as one of the maximum important segments for the prediction. data science and machine learning (ML) can be surprisingly supportive within the prediction of coronary heart attacks in which numerous hazard additives like excessive blood pressure, high ldl cholesterol, abnormal pulse rate, diabetes, etc. can be considered. The objective of this study is to optimize the prediction of heart attack using machine learning. This work provides a few system learning processes for predicting heart attack, the use of information of primary health variables from patients. The paper demonstrated four classification methods: Logistic Regression, support Vector Classifier, Random forest Classifier, and Naïve Bayes (NB), to build the prediction models. data preprocessing and characteristic selection steps had been performed earlier than building the models. The models have been evaluated primarily based on the precision, accuracy, recall, and F1- score. The Bolster Vector Classifier demonstrate completed exceptional with 92.19% accuracy. |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
English |
| Relation: |
https://zenodo.org/communities/elepub; https://doi.org/10.5281/zenodo.12663003; https://doi.org/10.5281/zenodo.12663004; oai:zenodo.org:12663004 |
| DOI: |
10.5281/zenodo.12663004 |
| الاتاحة: |
https://doi.org/10.5281/zenodo.12663004 |
| Rights: |
info:eu-repo/semantics/openAccess ; Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/legalcode |
| رقم الانضمام: |
edsbas.8E82E337 |
| قاعدة البيانات: |
BASE |