Early detection of heart disease using Random Forest Algorithm / Muhammad Iqbal Suhaidin ... [et al.]
The prediction of heart diseases is a crucial aspect of healthcare, as it helps medical professionals to diagnose and treat the condition at an early stage. This is a preliminary study that aims to investigate the Random Forest Algorithm (RFA) that accurately predicts the presence of heart diseases,...
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| Format: | Livre |
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Unit Penerbitan JSKM,
2023-04.
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| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | repouitm_83497 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Suhaidin, Muhammad Iqbal |e author |
| 700 | 1 | 0 | |a Mohamed Yusoff, Syarifah Adilah |e author |
| 700 | 1 | 0 | |a Johan, Elly Johana |e author |
| 700 | 1 | 0 | |a Mydin, Azlina |e author |
| 700 | 1 | 0 | |a Wan Mohamad, Wan Anisha |e author |
| 245 | 0 | 0 | |a Early detection of heart disease using Random Forest Algorithm / Muhammad Iqbal Suhaidin ... [et al.] |
| 260 | |b Unit Penerbitan JSKM, |c 2023-04. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/83497/1/83497.pdf | ||
| 520 | |a The prediction of heart diseases is a crucial aspect of healthcare, as it helps medical professionals to diagnose and treat the condition at an early stage. This is a preliminary study that aims to investigate the Random Forest Algorithm (RFA) that accurately predicts the presence of heart diseases, enabling healthcare providers to take proactive measures to prevent severe health complications and improve patient outcomes. RFA as a machine learning classification model has the potential to provide more accurate predictions than traditional methods. This potential has been investigated by thoroughly compared with several other studies across implementation of different types of dataset and algorithms. Furthermore, additional prototypes could be used in clinical settings, providing valuable insights to healthcare providers and contributing to the advancement of medical research | ||
| 546 | |a en | ||
| 690 | |a Evolutionary programming (Computer science). Genetic algorithms | ||
| 655 | 7 | |a Article |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/83497/ | |
| 787 | 0 | |n https://appspenang.uitm.edu.my/sigcs/ | |
| 856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/83497/ |z Link Metadata |