Toward Comprehensive Chronic Kidney Disease Prediction Based on Ensemble Deep Learning Models
Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 13, Issue: 6
2023
- 24Citations
- 46Captures
- 2Mentions
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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.
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