A hybrid machine learning framework to predict mortality in paralytic ileus patients using electronic health records (EHRs)-Reference-Cited by-同舟云学术

A hybrid machine learning framework to predict mortality in paralytic ileus patients using electronic health records (EHRs)

Published:2020-08-10 Issue:3 Volume:12 Page:3283-3293
ISSN:1868-5137
Container-title:Journal of Ambient Intelligence and Humanized Computing
language:en
Short-container-title:J Ambient Intell Human Comput

Author:

Ahmad Fahad Shabbir,Ali Liaqat,Raza-Ul-Mustafa ,Khattak Hasan Ali,Hameed Tahir,Wajahat Iram,Kadry Seifedine,Bukhari Syed Ahmad Chan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s12652-020-02456-3.pdf

Reference56 articles.

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2. Ali L, Khan SU, Golilarz NA, Yakubu I, Qasim I, Noor A, Nour R (2019a) A feature-driven decision support system for heart failure prediction based on statistical model and Gaussian naive bayes. Computational and Mathematical Methods in Medicine, 2019

3. Ali L, Niamat A, Golilarz NA, Ali A, Xingzhong X (2019b) An expert system based on optimized stacked support vector machines for effective diagnosis of heart disease. IEEE Access

4. Ali L, Niamat A, Khan JA, Golilarz NA, Xingzhong X, Noor A, Nour R, Bukhari SAC (2019c) An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access 7:54007–54014

5. Ali L, Wajahat I, Golilarz NA, Keshtkar F, Bukhari SAC (2020) Lda–ga–svm: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine. Neural Comput Appl, pp. 1–10

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