Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model

Big Data and Cognitive Computing, Год журнала: 2023, Номер 7(3), С. 144 - 144

Опубликована: Авг. 16, 2023

Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially cases such as kidney disease (CKD). Machine learning (ML) techniques have emerged valuable tools for reducing randomness enhancing clinical decision-making. However, conventional methods CKD detection lack accuracy due their reliance on limited sets of biological attributes. This research proposes a novel ML model predicting CKD, incorporating various preprocessing steps, feature selection, hyperparameter optimization technique, algorithms. To address challenges medical datasets, we employ iterative imputation missing values sequential approach data scaling, combining robust z-standardization, min-max scaling. Feature selection performed using the Boruta algorithm, developed The proposed was validated UCI dataset, achieving outstanding performance with 100% accuracy. Our approach, innovative k-nearest neighbors along grid-search cross-validation (CV), demonstrates its effectiveness early CKD. highlights potential improving support systems impact prognosis.

Язык: Английский

---

Detection of Kidney Diseases

International Journal of E-Health and Medical Communications, Год журнала: 2024, Номер 15(1), С. 1 - 21

Опубликована: Сен. 13, 2024

Chronic kidney disease (CKD) is a medical condition characterized by impaired function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early diagnosis and treatment have been made. However, as time-consuming, an automated system necessary. Researchers employing various machine learning approaches analyze extensive complex data, aiding clinicians predicting CKD enabling intervention. Identifying the most crucial attributes for this paper's primary objective. address gap, six nature-inspired algorithms nine classifiers were compared evaluate their combined effectiveness detecting CKD. A benchmark dataset from UCI repository was utilized analysis. The proposed model outperforms other with remarkable 99.5% accuracy rate; it also achieves 58% reduction feature dimensionality. By providing reliable, cost-effective tool detection, authors aim revolutionize patient care.

Язык: Английский

---

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Computer Modeling in Engineering & Sciences, Год журнала: 2024, Номер 139(3), С. 3513 - 3534

Опубликована: Янв. 1, 2024

Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis.Machine learning has emerged as powerful tool for detection, medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine techniques on dataset obtained from University California, UC Irvine Machine Learning repository.The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, extra tree classifier, which excels in providing highly predictions CKD.Furthermore, K nearest neighbor (KNN) imputer utilized deal with missing values while synthetic minority oversampling (SMOTE) used class-imbalance problems.To ascertain efficacy proposed model, comprehensive comparative analysis conducted various models.The TrioNet KNN SMOTE outperformed other models 98.97% accuracy detecting CKD.This in-depth demonstrates model's capabilities underscores its potential valuable diagnosis CKD.

Язык: Английский

---

On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence

Intelligent Systems with Applications, Год журнала: 2024, Номер 22, С. 200397 - 200397

Опубликована: Июнь 1, 2024

Chronic Kidney Disease (CKD) is increasingly recognised as a major health concern due to its rising prevalence. The average survival period without functioning kidneys typically limited approximately 18 days, creating significant need for kidney transplants and dialysis. Early detection of CKD crucial, machine learning methods have proven effective in diagnosing the condition, despite their often opaque decision-making processes. This study utilised explainable predict CKD, thereby overcoming 'black box' nature traditional predictions. Of six algorithms evaluated, extreme gradient boost (XGB) demonstrated highest accuracy. For interpretability, employed Shapley Additive Explanations (SHAP) Partial Dependency Plots (PDP), which elucidate rationale behind predictions support process. Moreover, first time, graphical user interface with explanations was developed diagnose likelihood CKD. Given critical high stakes use can aid healthcare professionals making accurate diagnoses identifying root causes.

Язык: Английский

---

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

Applied Sciences, Год журнала: 2023, Номер 13(6), С. 3937 - 3937

Опубликована: Март 20, 2023

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

Язык: Английский

---

A two-stage feature selection approach using hybrid quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification

Applied Soft Computing, Год журнала: 2023, Номер 146, С. 110704 - 110704

Опубликована: Авг. 9, 2023

Язык: Английский

---

Big Data Analytics Using Artificial Intelligence

Electronics, Год журнала: 2023, Номер 12(4), С. 957 - 957

Опубликована: Фев. 15, 2023

Data analytics using artificial intelligence is the process of leveraging advanced AI techniques to extract insights and knowledge from large complex datasets [...]

Язык: Английский

---

Chronic Kidney Disease Detection Using GridSearchCV Cross Validation Method

Опубликована: Май 1, 2023

Kidney disease is a serious public health issue that spreading around the world. According to estimates, 10% of people globally suffer from chronic kidney disease, which one main causes mortality and disability. Hence, for early identification, prevention, treatment, precise prediction renal crucial. Overall, important research because it can improve patient outcomes, tailor care, lead creation fresh preventative therapeutic approaches. In order forecast illness this study's GridSearchCV with 10-fold cross-validation, we must first import required libraries load dataset. Secondly, dividing dataset into features labels prepare modelling. We created pipeline comprises preprocessing procedures machine learning algorithm after data training testing sets. Then, using fit object establishing hyperparameters search over it. Lastly, forecasted on test set best estimator discovered by GridSearchCV, assessed model's performance measures like accuracy, precision, recall.

Язык: Английский

---

HDLNET: A Hybrid Deep Learning Network Model With Intelligent IOT for Detection and Classification of Chronic Kidney Disease

IEEE Access, Год журнала: 2023, Номер 11, С. 99638 - 99652

Опубликована: Янв. 1, 2023

Over 10% of the world's population now suffers from chronic kidney disease (CKD), and millions die yearly. CKD should be detected early to extend lives those suffering lower cost therapy. Building such a multimedia-driven model is necessary detect illness effectively accurately before it worsens situation. It challenging for doctors identify various conditions connected prevent condition. This study introduces novel hybrid deep learning network (HDLNet) detection prediction. A learning-based technique called Deep Separable Convolution Neural Network (DSCNN) has been suggested in this research CKD. More processing attributes characteristics chosen indicate issue are extracted by Capsule (CapsNet). Using Aquila Optimization Algorithm (AO) method, pertinent selected speed up categorization process. The features improve classification effectiveness while needing less computational effort. DSCNN optimized diagnose as or non-CKD using Sooty Tern (STOA). dataset, found UCI machine repository, then used test dataset. Accuracy, sensitivity, MCC, PPV, FPR, FNR, specificity performance metrics approach. Additional experimental findings demonstrate that method produces better than present state-of-the-art method.

Язык: Английский

---

Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease

e-Prime - Advances in Electrical Engineering Electronics and Energy, Год журнала: 2024, Номер 7, С. 100463 - 100463

Опубликована: Фев. 9, 2024

A steady deterioration in kidney function over months or years is known as chronic disease (CKD). Through a range of techniques, such pharmacological intervention moderate cases and hemodialysis renal transport severe cases, early identification CKD crucial has substantial influence on reducing the patient's health development. The outcomes show kidneys' present state. It suggested to develop system for detecting using machine learning. Finding best feature sets typically involves metaheuristic algorithms since selection an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) frequently used both local global searches. In this study, we employ brand-new hybrid TS stochastic diffusion (SDX)-based selection. adaptive backpropagation neural network (ABPNN-ANFIS) then classified fuzzy logic. Fuzzy logic may be combine ABPNN findings. Consequently, these techniques can aid experts determining stage disease. Adaptive Neuron Clearing Inference System was utilised inverse networks MATLAB programme. demonstrate that ABPNN-ANFIS 98% accurate terms efficiency.

Язык: Английский

---