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Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 13, Issue: 6
2023
  • 24
    Citations
  • 0
    Usage
  • 46
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    24
    • Citation Indexes
      24
  • Captures
    46
  • Mentions
    2
    • Blog Mentions
      1
      • 1
    • News Mentions
      1
      • 1

Most Recent Blog

Applied Sciences, Vol. 13, Pages 3937: Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models

Applied Sciences, Vol. 13, Pages 3937: Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models Applied Sciences doi: 10.3390/app13063937 Authors: Deema Mohammed

Most Recent News

Princess Nourah bint Abdulrahman University Researcher Has Provided New Study Findings on Chronic Kidney Disease (Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models)

2023 APR 04 (NewsRx) -- By a News Reporter-Staff News Editor at Gastroenterology Daily News -- New research on chronic kidney disease is the subject

Article Description

Chronic kidney disease (CKD) refers to the gradual decline of kidney function over months or years. Early detection of CKD is crucial and significantly affects a patient’s decreasing health progression through several methods, including pharmacological intervention in mild cases or hemodialysis and kidney transportation in severe cases. In the recent past, machine learning (ML) and deep learning (DL) models have become important in the medical diagnosis domain due to their high prediction accuracy. The performance of the developed model mainly depends on choosing the appropriate features and suitable algorithms. Accordingly, the paper aims to introduce a novel ensemble DL approach to detect CKD; multiple methods of feature selection were used to select the optimal selected features. Moreover, we study the effect of the optimal features chosen on CKD from the medical side. The proposed ensemble model integrates pretrained DL models with the support vector machine (SVM) as the metalearner model. Extensive experiments were conducted by using 400 patients from the UCI machine learning repository. The results demonstrate the efficiency of the proposed model in CKD prediction compared to other models. The proposed model with selected features using mutual_info_classi obtained the highest performance.

Bibliographic Details

Deema Mohammed Alsekait; Lubna Abdelkareim Gabralla; Hager Saleh; Khaled Alnowaiser; Shaker El-Sappagh; Radhya Sahal; Nora El-Rashidy

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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