Feature Selection and Ensemble Entropy Attribute Weighted Deep Neural Network (EEAw-DNN) for Chronic Kidney Disease (CKD) Prediction

2020 ◽  
pp. 1232-1247
Author(s):  
S. Belina V. J. Sara ◽  
K. Kalaiselvi
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Deep Neural Network

scholarly journals PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK

2021 ◽  
Vol 4 (4) ◽  
pp. 34-41
Author(s):  
Iliyas Ibrahim Iliyas ◽  
Saidu Isah Rambo ◽  
Ali Baba Dauda ◽  
Suleiman Tasiu
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Heart Disease ◽  
Kidney Disease ◽  
General Hospital ◽  
Deep Neural Network ◽  
Disease Prediction ◽  
Technical Approach ◽  
Medical Practitioners ◽  
Features Importance

eural Network (DNN) is now applied in disease prediction to detect various ailments such as heart disease and diabetes. Another disease that is causing a threat to our health is kidney disease. This disease is becoming prevalent due to substances and elements we intake. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized of late. We conducted our research on CKD in Bade, a Local Government Area of Yobe State in Nigeria. The area has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 1200 patients with 10 attributes as our dataset from Bade General Hospital and used the DNN model to predict CKD's absence or presence in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to rank the features used in predicting the CKD. The outcome revealed that two attributes: Creatinine and Bicarbonate, have the highest influence on the CKD prediction.

scholarly journals A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 116
Author(s):  
Vijendra Singh ◽  
Vijayan K. Asari ◽  
Rajkumar Rajasekaran
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Early Detection ◽  
Deep Neural Network ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Recursive Feature Elimination ◽  
Support Vector

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network’s optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD.

scholarly journals Chronic Kidney Disease Diagnosis System using Sequential Backward Feature Selection and Artificial Neural Network

2021 ◽  
Vol 317 ◽  
pp. 05030
Author(s):  
Siti Noor Chotimah ◽  
Budi Warsito ◽  
Bayu Surarso
Keyword(s):  
Neural Network ◽  
Chronic Kidney Disease ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Kidney Disease ◽  
Early Stage ◽  
Final Diagnosis ◽  
Disease Diagnosis ◽  
Study Results ◽  
Artificial Neural

The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.

scholarly journals Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li-Hsin Cheng ◽  
Te-Cheng Hsu ◽  
Che Lin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Feature Selection ◽  
Systems Biology ◽  
Ensemble Learning ◽  
Microarray Data ◽  
Deep Neural Network ◽  
Prediction Models ◽  
Biological Knowledge ◽  
Prognosis Prediction

AbstractBreast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power. Moreover, to capture breast cancer's complex molecular processes, we adopted a multi-gene approach to predict the prognosis status using deep learning classifiers. We found that all ensemble approaches could improve feature selection robustness, wherein the hybrid ensemble approach led to the most robust result. Among all prognosis prediction models, the bimodal deep neural network (DNN) achieved the highest test performance, further verified by survival analysis. In summary, this study demonstrated the potential of combining ensemble learning and bimodal DNN in guiding precision medicine.

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