IRJET-Survey on Chronic Kidney Disease Prediction System with Feature Selection and Feature Extraction using Machine Learning Technique (original) (raw)
Related papers
2020
1, 2, 3Department of Computer Science and Engineering, Agni College of Technology 4Assistant professor, Computer Science and Engineering Department, Agni College of technology ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Chronic Kidney Disease (CKD) need to be diagnosed earlier before kidneys fail to work.In order to help doctors or medical experts in prediction of CKD among patients easily, this paper has developed an intelligent system named Chronic Kidney Disease Prediction System (CKDPS) that can predict CKD among patients. The proposed system predict the CKD with minimal feature input instead of dumping all the features which may not relevant to predict the disease.To achieve this we have planned to approach by three feature selection algorithm with combination of two feature Extraction algorithm.After performing feature selection and Feature Extraction, those features will ...
Chronic Kidney Disease Prediction System Using Machine Learning
International Journal for Research in Applied Science and Engineering Technology
Chronic kidney disease (CKD) is a life-threatening condition that can be difficult to diagnose early because there are no symptoms. The purpose of the proposed study is to develop and validate a predictive model for the prediction of chronic kidney disease. Machine learning algorithms are often used in medicine to predict and classify diseases. Medical records are often skewed. Chronic Kidney Disease (CKD) or chronic renal disease has become a significant issue with a steady growth rate. A person can only survive without kidneys for an average of 18 days, which makes a huge demand for a kidney transplant and Dialysis. It is important to have effective methods for the early prediction of CKD. Machine learning methods are effective in CKD prediction. This work proposes a workflow to predict CKD status based on clinical data, incorporating data prepossessing, a missing value handling method with collaborative filtering and attribute selection. Out of the 11 machine learning methods considered, the extra tree classifier and random forest classifier are shown to result in the highest accuracy and minimal bias to the attributes. The project also considers the practical aspects of data collection and highlights the importance of incorporating domain knowledge when using machine learning for CKD status prediction. I.
Human-Centric Intelligent Systems
Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine ...
Chronic Kidney Disease Prediction using Machine Learning Algorithms
International Journal of Preventive Medicine and Health, 2021
Kidney diseases are increasing day by day among people. It is becoming a major health issue around the world. Not maintaining proper food habits and drinking less amount of water are one of the major reasons that contribute this condition. With this, it has become necessary to build up a system to foresee Chronic Kidney Diseases precisely. Here, we have proposed an approach for real time kidney disease prediction. Our aim is to find the best and efficient machine learning (ML) application that can effectively recognize and predict the condition of chronic kidney disease. We have used the data from UCI machine learning repository. In this work, five important machine learning classification techniques were considered for predicting chronic kidney disease which are KNN, Logistic Regression, Random Forest Classifier, SVM and Decision Tree Classifier. In this process, the data has been divided into two sections. In one section train dataset got trained and another section got evaluated ...
Zenodo (CERN European Organization for Nuclear Research), 2020
Chronic kidney disease (CKD) is considered as a lethal disease all over the world. Chronic kidney disease (CKD) is a condition where kidney shrinks in size and also changes its natural shape. Various machine learning algorithms can be very useful for prediction of CKD. This paper investigates the performance of various machine learning algorithm on chronic kidney disease dataset. Support Vector Machine (SVM), Decision Tree, Naïve Bayes, Random Forest and Logistic Regression are the algorithms considered in this paper. Initially a dataset of 400 instances having 24 attributes is considered. Later feature selection algorithm is used to identify the important attributes and we reduced the uncorrelated attributes and observed the results. Results show that Naïve Bayes achieved the maximum accuracy of 99.1% on reduced chronic kidney disease dataset of 23 attributes. In terms of time complexity decision tree performed better than the other classifiers. It is expected that the application of different machine learning algorithms can help to predict CKD with great accuracy in practice.
Diagnosis of Chronic Kidney Disease Using Random Forest Classification Technique
Diagnosing a disease is a complicated task in many existing medical expert systems, Diagnosing a disease is based on the patient symptoms and other details that are given as input to the system. Several levels of uncertainty are involved in medical diagnosis. Data mining is a dominant research area to diagnose the medical diseases. Random Forest is one of the classification technique used to diagnose chronic kidney disease. UCI is a Machine learning Repository which maintains a large collection of medical datasets. Chronic kidney disease data set is one of the dataset used for the proposed model. The proposed work shows the Random Forest Classification is best suitable classifier for Chronic Kidney Disease Dataset among all classifiers like Naive Bayes, SMO, J48 et.c., With these proposed method an Diagnosis system is developed to produce results to end users.
2018
the main objective of data mining is to mine data from a huge number of datasets and transform this mining data into a meaning full structure for representing the real object. Data mining technique is used to manage different organizational data to analyse and predict. Data mining classification techniques used to the management of healthcare data and play a major role in diagnosis and predict the survivalist of a different disease. The objective of this work is to analyse and predict chronic kidney disease (CKD) by discovering the hidden pattern of the relationship that is directly related with CKD by using feature selection and data mining classification techniques like k-nearest neighbor (KNN), artificial neural network (ANN), decision tree. The work will show that feature selection and classification based methods to enhance the performance accuracy of the algorithm to effective analysis and prediction of chronic kidney disease. Keywords— Chronic Kidney Disease, Data Mining, Cla...
Chronic Kidney Disease Prediction Using Different Algorithms
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020
Chronic Kidney Disease (CKD) is a disease which doesn't shows symptoms at all or in some cases it doesn't show any disease specific symptoms it is hard to predict, detect and prevent such a disease and this could be lead to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. The objective of paper is to build the model for predicting the Chronic Kidney Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of Chronic Kidney Disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model.
Prediction of Chronic Kidney Disease using Data Mining Feature Selection and Ensemble Method
WSEAS Transactions on Information Science and Applications archive, 2018
The failure of the kidney is affected the whole human body and it can be a cause of the seriously ill and cause of deaths. Machine learning and data mining techniques are the most significant role in disease prediction with high-performance rate and used to help decision makers to assemble and understand information. The performance of classification techniques depends on the feature of the data set. To improve the accuracy of classification used feature selection method by reducing the dimensions of the feature and used ensemble or combine a model of the algorithm. In this research K-Nearest Neighbor, J48, Artificial Neural Network, Naive Bayes and Support Vector Machine classification techniques were used to diagnose Chronic Kidney Disease. To predict chronic kidney disease, build two important models. Namely, feature selection method and ensemble model. To build chronic kidney disease prediction, used Info gain attributes evaluator with ranker search engine and wrapper subset eva...
Fused Features Classification for the Effective Prediction of Chronic Kidney Disease
The paper presents an application of data mining for improving the accuracy of prediction of a disease state by selecting the most relevant features associated with it. The experiments are performed on chronic kidney disease (CKD) data. The basic idea in this study is that use of a number of methods instead of a single one increases the probability of selecting features which are more closely related to the disease. Multiple feature selection methods have been applied independently on the CKD data set and the results integrated into a final optimal set of features. These data have been applied to the classifiers to identify CKD from reference cases. Various classification methods are compared to select the best model over 10-fold cross-validation in the training data set. Random Forest classifier is chosen as the best model with superior performance.