One of the most crucial problems with artificial intelligence systems is thought to be the identification of correct kidney diseases through machine learning. Manual diagnosis for predicting the kidney disease by doctors is time consuming and may raise the workload on doctors. So, the developed system uses a machine learning technique for predicting the chronic kidney disease which may help the doctors in early prediction of the kidney disease. In order to diagnose chronic kidney disease four Machine Learning technique namely Naïve Bayes, Random Forest, Decision Tree and Support Vector Machine is used. Naive Bayes uses probability to forecast kidney disease, whereas decision trees are used to generate categorized reports for the disease. This system will compare the accuracy score of each Machine Learning technique. Hence, Random Forest gives the better performance compared to other classification methods with accuracy score of 98.75 % , <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F1}=\mathbf{score}=\boldsymbol{99\%},\mathbf{ Precision}=\boldsymbol{99\%},\mathbf{Recall} =\boldsymbol{99\%}$</tex> . This paper shows the efficiency and accuracy of the predicted chronic kidney disease.

Prediction of Chronic Kidney Disease Using Machine Learning Technique

Chronic kidney disease (CKD) slowly decreases one’s kidney ability. A machine learning (ML) based early CKD diagnosis scheme can be an effective solution to reduce this harm. The efficiency of ML techniques depends on the selection and use of the appropriate features. Hence, this research analysis several feature optimization approaches along with a max voting ensemble model to establish a highly accurate CKD diagnosis system by using an appropriate feature set. The ensemble model of this research is structured with five existing classifiers. Three types of feature optimization namely feature importance, feature reduction, and feature selection where for each approach two most proficient techniques are analyzed with the mentioned ensemble model. Based on all analysis the research gets a feature optimization technique called Linear discriminant analysis belonging to the feature selection approach provides the most outstanding result of 99.5% accuracy by using 10-fold cross-validation. The results of this research indicate the efficiency of feature optimization for the diagnosis of ML-based CKD. 

Analysis of the performance of feature optimization techniques for the diagnosis of machine learning-based chronic kidney disease

Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study

Aim: The primary objective of this research is to increase accuracy in the prediction of chronic kidney disease (CKD) by using Machine Learning (ML) algorithms, including K-Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks algorithm. Methods and Materials: The proposed work included four groups. Group 1 refers to a set of four different ensemble tree ML algorithms (Random Forest, Extra Trees, AdaBoost, and XGBoost) that were used to obtain the optimal classification model to support CKD early diagnosis; Group 2 refers to the K-Nearest Neighbors algorithm, which can be used to handle missing values; Group 3 uses the Support Vector Machine algorithm to classify patients into CKD or non-CKD categories; and Group 4 refers to the Artificial Neural Networks algorithm that analyses medical data to predict CKD. Results: The proposed system improves chronic kidney disease prediction, achieving 99.2% accuracy for early detection and management on an automated platform. Conclusion: All three models, including KNN, SVM and ANN, have demonstrated their potential in accurately predicting CKD with an average accuracy of 99.2%, which performs better than four different ensemble tree ML algorithms.

A Machine Learning Perspective for Predicting Chronic Kidney Disease

Deep learning (DL) classification has become a major research topic in the areas of cancer prediction, image cell classification, and image classification in medicine.Furthermore, DL classification is the core of other subfields.Owing to various forms of ensemble models, DL models have achieved state-of-theart performances in fields such as medicine.However, the existing models cannot solve the problem of generalization perfectly and proposed solutions only for tasks with specific datasets.Most state-of-the-art classification models presented their results in ImageNet dataset, and models elaborate on the insights of the dataset.Nonetheless, model architectures or pretrained models cannot provide the same accurate results for datasets with different classes than ImageNet.Hence, this research proposes an improved convolutional neural network ensemble (ICNN-Ensemble) based on the representation of high-resolution image channels (RHRIC) and a systematic model dropout ensemble (SMDE).ICNN-Ensemble exploits image channels after applying RHRIC and RGB images in their original forms, which accesses more residual feature connections and represents more insight into image channels.Furthermore, SMDE is applied to choose ensemble members, considering the changes of the accurate prediction field (APF) in the ICNN-Ensemble model.In addition, the proposed model executes ensembling during the test set prediction, which allows the model to be trained with larger batches and images compared to ensemble model's final results during training, allowing maximal effective usage of the graphics processing unit (GPU).Despite the small size of the model, the results of benchmarking for Malaria cell images dataset clearly illustrated that the ICNN-Ensemble model achieved significantly more accurate results than other base state-of-the-art models. 

ICNN-Ensemble: An Improved Convolutional Neural Network Ensemble Model for Medical Image Classification

Chronic Kidney Disease is one among the non-contagious illnesses that affect most of the individual in the world. The main factors of risk for the Chronic Kidney Disease are Diabetes, Heart Ailment, Hypertension. The Chronic Kidney Disease shows no symptoms in the early stages and most of the cases are diagnosed in the advanced stage. This leads to delayed treatment to the patient which may be fatal. Machine learning technique provides an efficient way in the prediction of Chronic Kidney Disease at the earliest stage. In this paper, four ensemble algorithms are used to diagnose the patient with Chronic Kidney Disease at the earlier stages. The machine learning models are evaluated based on seven performance metrics including Accuracy, Sensitivity, Specificity, F1-Score, and Mathew Correlation Coefficient. Based on the evaluation the AdaBoost and Random Forest performed the best in terms of accuracy, precision, Sensitivity compared to Gradient Boosting and Bagging. The AdaBoost and Random Forest also showed the Mathew Correlation Coefficient and Area Under the curve scores of 100%. The machine learning model proposed in this paper will provide an efficient way to prevent Chronic Kidney diseases by enabling the medical practitioners to diagnose the disease at an early stage.

Chronic Kidney Disease Prediction using Machine Learning Ensemble Algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Nikhila

Author Tools

Chat about Author

Papers

Chronic Kidney Disease Prediction using Machine Learning Ensemble Algorithm