Галерея 2557795

Галерея 2557795

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1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

5 College of Technological Innovation, Zayed University, Abu Dhabi, UAE.

Zahid Ullah et al.

Comput Intell Neurosci .

2022 .

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Affiliations

1 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

4 Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

5 College of Technological Innovation, Zayed University, Abu Dhabi, UAE.

[No authors listed]
[No authors listed]
Ann Clin Lab Sci. 2022 May;52(3):511-525.
Ann Clin Lab Sci. 2022.

PMID: 35777803

No abstract available.

Yang F, Wang K, Sun L, Zhai M, Song J, Wang H.
Yang F, et al.
BMC Med Inform Decis Mak. 2022 Dec 29;22(1):344. doi: 10.1186/s12911-022-02075-2.
BMC Med Inform Decis Mak. 2022.

PMID: 36581862
Free PMC article.

Ullah Z, Jamjoom M.
Ullah Z, et al.
J Healthc Eng. 2023 Jan 30;2023:3553216. doi: 10.1155/2023/3553216. eCollection 2023.
J Healthc Eng. 2023.

PMID: 36756136
Free PMC article.

Nagavelli U, Samanta D, Chakraborty P.
Nagavelli U, et al.
J Healthc Eng. 2022 Feb 27;2022:7351061. doi: 10.1155/2022/7351061. eCollection 2022.
J Healthc Eng. 2022.

PMID: 35265303
Free PMC article.

Wang K, Tian J, Zheng C, Yang H, Ren J, Li C, Han Q, Zhang Y.
Wang K, et al.
Risk Manag Healthc Policy. 2021 Jun 8;14:2453-2463. doi: 10.2147/RMHP.S310295. eCollection 2021.
Risk Manag Healthc Policy. 2021.

PMID: 34149290
Free PMC article.

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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.

Copyright © 2022 Zahid Ullah et al.

There are no conflicts of interest.

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Datasets ▶ Files ▶ MD5 51748da4eb0fd2bf6f2932e33763c76e
English [en], epub, 3.8MB, The Cambridge History of China_ Volume 2, - Various.epub
The Cambridge History of China: Volume 2, The Six Dynasties, 220–589
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Files from shadow libraries, combined by MD5

The Cambridge History of China_ Volume 2, - Various.epub

The Cambridge History of China: Volume 2, The Six Dynasties, 220–589

Book (non-fiction) ("book_nonfiction")

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Бразильские шалавы
Сочная голая негритянка из Пенсильвании
Две нежные лесбиянки показывают как можно ублажать друг друга при этом не используя ничего постороннего

Галерея 2557795

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