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Prediction Heart Attack using Artificial Neural Networks (ANN)
International Journal of Engineering and Information Systems (IJEAIS) 7 (10):36-41 (2023)
Abstract
Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy of 98.01% on the Heart Disease Dataset.Author's Profile
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Citations of this work
Leveraging Artificial Neural Networks for Cancer Prediction: A Synthetic Dataset Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (11):43-51.
References found in this work
Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
The Relationship between Correcting Deviations in Measuring Performance and Achieving the Objectives of Control - The Islamic University as a Model.Abed Alfetah M. AlFerjany, Ashraf A. M. Salama, Youssef M. Abu Amuna, Mazen J. Al Shobaki & Samy S. Abu-Naser - 2018 - International Journal of Engineering and Information Systems (IJEAIS) 2 (1):74-89.
The Role of Administrative Procedures and Regulations in Enhancing the Performance of The Educational Institutions - The Islamic University in Gaza is A Model.Ashraf A. M. Salama, Youssef M. Abu Amuna, Mazen J. Al Shobaki & Samy S. Abu-Naser - 2018 - International Journal of Academic Multidisciplinary Research (IJAMR) 2 (2):14-27.
A Proposed Knowledge Based System for Desktop PC Troubleshooting.Ahmed Wahib Dahouk & Samy S. Abu-Naser - 2018 - International Journal of Academic Pedagogical Research (IJAPR) 2 (6):1-8.
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