Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches (original) (raw)
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Human-Centric Intelligent Systems
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Sci Rep, 2022
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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 ...
An Approach for improving the Prediction of Chronic Kidney Disease using Machine learning
International Journal of Scientific Research in Science, Engineering and Technology, 2020
According the 2010 global burden of disease study, Chronic Kidney Diseases (CKD) was ranked 18th in the list of causes of total no. of deaths worldwide. 10% of the population worldwide is affected by CKD. The prediction of CKD can become a boon for the population to predict the health. Various method and techniques are undergoing the research phase for developing the most accurate CKD prediction system. Using Machine learning techniques is the most promising one in this area due to its computing function and Machine learning rules. Existing Systems are working well in predicting the accurate result but still more attributes of data and complicity of health parameter make the root layer for the innovation of new approaches. This study focuses on a novel approach for improving the prediction of CKD. In our proposed system we will implement the deep learning algorithms like Deep Neural Network. Chronic kidney disease detection system using deep network is shown here. This system of deep network accepts disease-symptoms as input and it is trained according to various training algorithms. After the network is trained, this trained network system is used for detection of kidney disease in the human body.
Chronic Kidney Disease Prediction using Machine Learning Models
International Journal of Engineering and Advanced Technology, 2019
The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to pr...
International Journal of Scientific Research in Science and Technology, 2023
With a high rate of morbidity and mortality as well as the ability to spread other diseases, chronic kidney disease (CKD) is a major worldwide health concern. Patients sometimes overlook the disease in the early stages of CKD since there are no evident symptoms. Early diagnosis of CKD enables patients to receive effective treatment in time to slow the disease's progression. Due to their quick and precise detection capabilities, machine learning models can help therapists accomplish this goal efficiently. In this research, we suggest a machine learning approach to CKD diagnosis. The website KAGGLE provided the CKD data set, which has a significant number of missing values.. The mean value is used to fill in the blanks; for object data types (strings), we utilized the most frequent object (string) to replace the missing values. Since patients may overlook particular measurements for a variety of reasons, missing values are typically observed in real-world medical scenarios. Four machine learning algorithms—Logistic Regression, SVM, Random Forest Classifier, and Decision Tree Classifier—were applied to create models after successfully completing the incomplete data set. Random Forest has the highest accuracy of these machine learning models.
IJERT-Chronic Kidney Disease Prediction using Neural Network and ML Models
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/chronic-kidney-disease-prediction-using-neural-network-and-ml-models https://www.ijert.org/research/chronic-kidney-disease-prediction-using-neural-network-and-ml-models-IJERTCONV9IS08002.pdf In Today's world, everyone is conscious of health. Because of the most dominant IT lifestyle, the workload is more and people hardly give attention to health unless it turns worse. Chronic kidney disease is a kind of disease that hardly shows symptoms in the early stages and in later stages, things become worse which might end in kidney failure or an artificial support system. Thus our system aims at predicting the disease early and will help in taking precautionary measures or early medication. We use three algorithms and analyze the performance of them. The algorithms used are support vector machine, random forest, and a hybrid neural network model