Items where Author is "Aziz, Firdaus"

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Number of items: 6.

Article

Kasim, Sazzli and Malek, Sorayya and Song, Cheen and Ahmad, Wan Azman Wan and Fong, Alan and Ibrahim, Khairul Shafiq and Safiruz, Muhammad Shahreeza and Aziz, Firdaus and Hiew, Jia Hui and Ibrahim, Nurulain (2022) In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. PLoS ONE, 17 (12). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0278944.

Lau, Chia Fong and Malek, Sorayya and Gunalan, Roshan and Chee, W. H. and Saw, A. and Aziz, Firdaus (2022) Paediatric upper limb fracture healing time prediction using a machine learning approach. ALll Life, 15 (1). pp. 490-499. ISSN 2689-5293, DOI https://doi.org/10.1080/26895293.2022.2064923.

Kasim, Sazzli and Malek, Sorayya and Cheen, Song and Safiruz, Muhammad Shahreeza and Wan Ahmad, Wan Azman and Ibrahim, Khairul Shafiq and Aziz, Firdaus and Negishi, Kazuaki and Ibrahim, Nurulain (2022) In-hospital risk stratification algorithm of Asian elderly patients. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-18839-9.

Aziida, Nanyonga and Malek, Sorayya and Aziz, Firdaus and Ibrahim, Khairul Shafiq and Kasim, Sazzli (2021) Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualisation. Sains Malaysiana, 50 (3). pp. 753-768. ISSN 0126-6039, DOI https://doi.org/10.17576/jsm-2021-5003-17.

Aziz, Firdaus and Malek, Sorayya and Ibrahim, Khairul Shafiq and Raja Shariff, Raja Ezman and Wan Ahmad, Wan Azman and Ali, Rosli Mohd and Liu, Kien Ting and Selvaraj, Gunavathy and Kasim, Sazzli (2021) Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach. PLoS ONE, 16 (8). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0254894.

Aziz, Firdaus and Malek, Sorayya and Ali, Adliah Mhd and Wong, Mee Sieng and Mosleh, Mogeeb and Milow, Pozi (2020) Determining hypertensive patients' beliefs towards medication and associations with medication adherence using machine learning methods. PeerJ, 8. ISSN 2167-8359, DOI https://doi.org/10.7717/peerj.8286.

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