PREDICTION AND PREVENTIVE AWARENESS OF CHRONIC KIDNEY DISEASE USING MACHINE LEARNING ALGORITHMS (original) (raw)
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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 ...
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 ...
IJARCCE, 2021
As per as the World Health Organization report is concern, about 10% of the world population is affected by chronic kidney disease (CKD), and millions die only because of inappropriate and non-affordable treatment. Kidney disease is a worldwide health crisis in the present scenario. This disease can be curable with early diagnosis and proper treatment. The purpose of this paper is to establish some predictive models using Machine Learning algorithms by taking a real time CKD dataset. In this paper, we have shown some real-time experiments and observations with the help of some Machine Learning algorithms, and also shown a clear picture on the predictive analysis on medical diagnosis of the chronic kidney disease (CKD) using Machine Learning algorithms using which patients may get accurate data so as to diagnose better for their early treatment.
Utilizing Machine Learning, Detect Chronic Kidney Disease and Suggest A Healthy Diet
IRJET, 2023
The UN's third sustainable development objective, "Good health and well-being," emphasizes the rising importance of non-communicable diseases. One of them is to reduce by half, by 2030, the rate of non-communicable diseaserelated premature death. Chronic kidney disease (CKD), one of the leading causes of morbidity and death from noncommunicable diseases, may affect 10-15% of the world's population. In order to minimize the effects of patient health complications like hypertension, anemia (low blood count), minerals bone disorder, poor nutritional health, and neurological complications with prompt intervention through appropriate medications, early and accurate detection of the stages of CKD is essential. Four prediction models are employed: Decision tree (DT), K-Nearest Neighbor (K-NN), Random Forest (RF), and Support Vector Machine (SVM). Evaluation of difference & recursion characteristics elimination using was employed for feature selection. Using tenfold cross-validation, the models were evaluated. Results of the experiment demonstrating the superior performance of RF utilizing recursive reduction of features with cross-validation over SVM and DT.
Survey for the Prediction of Chronic Kidney Disease using Machine Learning
International Journal of Scientific Research in Science, Engineering and Technology, 2019
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 recent time Neural network system has discovered its use in disease diagnoses, which is depended upon prediction from symptoms data set. Chronic kidney disease detection system using neural network is shown here. This system of neural network accepts disease-symptoms as input and it is trained according to various training algorithms. After neural network is trained using back propagation algorithms, this trained neural network system is used for detection of kidney disease in the human body.
Chronic Kidney Disease Prediction Using Machine Learning
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
IEEE, 2020
Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients' lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.
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...
Predicting the Kidney Diseases by Using Machine Learning Techniques
ITM Web of Conferences
CKD (Chronic Kidney Diseases) is a persistent medical state categorized by the kidney damage that hinders their ability to effectively filter blood. Over time, this progressive disease can result in kidney failure. This project compares the performance of the Support Vectos Machines (SVM), logistic regression and Decision Tree algorithms for predicting the risk of CKD. In this project, the dataset utilized comprises a total of 25 attributes, consisting of 11 numerical features and 14 nominal features. In the training of machine learning algorithms for prediction, all 400 instances from the dataset are utilized. Among these instances, 250 are labeled as CKD cases, indicating the presence of chronic kidney disease, while the remaining 150 instances are categorized as non-CKD cases, denoting the absence of the condition. We utilized the UCI dataset, which underwent preprocessing to handle missing data. Using Python, we trained and built Support Vectors Machines (SVM), Logistic Regressi...