Novel machine learning model with wrapper-based dimensionality reduction for predicting chronic kidney disease risk
Background The symptoms of chronic diseases (CDs) often appear too late and several
patients face unavoidable pain and expensive medical treatments. Chronic kidney disease …
patients face unavoidable pain and expensive medical treatments. Chronic kidney disease …
Predicting chronic kidney disease using machine learning algorithms
In the modern era, everyone tries to be aware of their health, but because of their workload
and hectic schedules, they only pay attention to it when certain symptoms appear. However …
and hectic schedules, they only pay attention to it when certain symptoms appear. However …
Development and implementation of patient-level prediction models of end-stage renal disease for type 2 diabetes patients using fast healthcare interoperability …
This study aimed to develop a model to predict the 5-year risk of developing end-stage renal
disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning …
disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning …
[PDF][PDF] Using ensemble learning and advanced data mining techniques to improve the diagnosis of chronic kidney disease
Kidney failure is a condition with far-reaching, potentially life-threatening consequences on
the human body. Leveraging the power of machine learning and data mining, this research …
the human body. Leveraging the power of machine learning and data mining, this research …
Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms
S Pal - Multimedia Tools and Applications, 2023 - Springer
Chronic kidney disease (CKD) is a common disease as it is difficult to diagnose early due to
its lack of symptoms. The main goal is to first diagnose kidney failure, which is a requirement …
its lack of symptoms. The main goal is to first diagnose kidney failure, which is a requirement …
[Retracted] Establishment and Evaluation of Artificial Intelligence‐Based Prediction Models for Chronic Kidney Disease under the Background of Big Data
X Yan, X Li, Y Lu, D Ma, S Mou, Z Cheng… - Evidence‐Based …, 2022 - Wiley Online Library
Objective. To establish a prediction model for the risk evaluation of chronic kidney disease
(CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients …
(CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients …
Machine learning techniques for chronic kidney disease risk prediction
Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney
function over time. It describes a clinical entity that causes kidney damage and affects the …
function over time. It describes a clinical entity that causes kidney damage and affects the …
A deep prediction of chronic kidney disease by employing machine learning method
In recent years, people worldwide have been suffering from various types of kidney diseases
indescribably, among which chronic kidney disease (CKD) has exacerbated the situation …
indescribably, among which chronic kidney disease (CKD) has exacerbated the situation …
Using random forest algorithm for glomerular and tubular injury diagnosis
W Song, X Zhou, Q Duan, Q Wang, Y Li, A Li… - Frontiers in …, 2022 - frontiersin.org
Objectives Chronic kidney disease (CKD) is a common chronic condition with high
incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early …
incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early …
Data-driven early diagnosis of chronic kidney disease: development and evaluation of an explainable AI model
PA Moreno-Sánchez - IEEE Access, 2023 - ieeexplore.ieee.org
Chronic Kidney Disease (CKD) is currently experiencing a growing worldwide incidence
and can lead to premature mortality if diagnosed late, resulting in rising costs to healthcare …
and can lead to premature mortality if diagnosed late, resulting in rising costs to healthcare …