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

Abstract

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.

ML-CKDP: Machine learning-based chronic kidney disease prediction with smart web application

“…Islam et al (2023) 8 70:30 XgBoost 98.3 % R. Sawhney et al (2023) 9 70:30 ANN 100% Alsekait D.M. et al (2023) 6 80:20 DL with SVM 99.69 Arif M.S. et al (2023) 10 80:20 KNN 100 Poonia RC et al (2022) 11 80:20 LR 98.75 Pal S. (2022) 12 80:20 DT 97.23 K.M.…”

“… Alsekait D.M. et al (2023) 6 1. Data scaling was not employed, potentially affecting model performance.…”

“…Alsekait et al (2023) 6 developed an ensemble deep learning model to predict CKD using a dataset of 400 cases with 24 features. The process involved data preprocessing, including label encoding and outlier detection, followed by feature selection through methods like mutual information and Recursive Feature Elimination (RFE).…”

“…These models support medical professionals in making accurate and timely decisions. 6 Machine learning, an artificial intelligence branch, offers potential for early CKD detection, fostering efficient and expedient intervention. This field has become instrumental in disease diagnosis, utilizing historical patient data to predict new patient outcomes.…”

Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning

“…AI provides a wide range of approaches for analyzing complex data to advance understanding of the subject of COVID-19 [4][5][6][7]. AI employs machine learning (ML) and deep learning (DL) to produce algorithms that can be used in the clinical and biomedical fields for patient classification and stratification based on the pairing and processing of a wide range of available data sources, such as heart disease detection [8], polycystic ovary syndrome detection [9], and chronic kidney disease detection [10]. The most significant contribution is using AI to detect patients at higher risk early to treat those patients and control disease transmission.…”