Chronic Kidney Disease analysis using data mining classification techniques

Kunwar, Veenita; Chandel, Khushboo; Sabitha, A. Sai; Bansal, Abhay

doi:10.1109/confluence.2016.7508132

Cited by 116 publications

(40 citation statements)

References 29 publications

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“…In supervised data mining, there is a target variable that must be classified, estimated, and predicted. Having no target variables, unsupervised data mining is responsible for finding key patterns that do not belong to a specific variable (Kunwar et al, 2016).…”

Section: Data Miningmentioning

confidence: 99%

A Model for the Mental Health Evaluation of Skilled Workers in Technology-Based Companies

Yousefi

Vanani

2020

Preprint

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Human resources are considered as one of the most valuable assets of an organization. Therefore, the top priorities of organizational managers include paying attention to manpower and providing necessary facilities to bring about satisfaction with workplace and working conditions in the light of mental and physical health. Accordingly, mental health has partly been neglected by managers because it lacks concrete and tangible aspects on the contrary to other organizational dimensions such as buildings and machinery. This study was conducted to achieve the fundamental goal of identifying the aspects and characteristics affecting the mental health of employees at technology-based companies as well as the factors resulting in mental problems and disorders. It was also decided to develop a model to predict affliction or non-affliction with mental problems among employees in the long term. For this purpose, this study focused on data mining techniques in order to identify latent relationships and introduce a prediction model. Therefore, the important and major factors were identified as individual characteristics, occupational characteristics, organizational performance features, and organizational governing culture features in mental health. The analysis and evaluation of results indicated that Classification algorithms managed to predict the mental health of employees with nearly %85 of accuracy. Besides, Clustering algorithms succeeded in dividing the samples into high-risk, medium-risk, and low-risk classes. The designed models can help organizational managers identify the factors affecting the mental health of employees and predict the chances of affliction with mental health disorders to prevent destructive harm to employees and organizations.

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Section: Data Miningmentioning

confidence: 99%

A Model for the Mental Health Evaluation of Skilled Workers in Technology-Based Companies

Yousefi

Vanani

2020

Preprint

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“…The experimental outputs were tested in Rapidminer tool that showed that NaïveBayes algorithm produces much better results than Artificial Neural Networks(ANN). [25] Murat Koklu et al, have experimented on the dataset of CKD by taking four different classification algorithms. About 400 samples the dataset was used for the research.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

Kumar*¹,

Thangaraju²

2019

IJEAT

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Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.

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“…The Support Vector Machine (SVM) classifier gives better accuracy rate as 98.5% using filtered subset evaluator with Best First Search engine for diagnosis of CKD. They [7] have used two classification There [8] are three classifiers namely Naive Bayes, J48 and SMO have used for classify the CKD where J48 is the most excellent performing algorithm out of three algorithms used for identifying Chronic Kidney Disease. Authors [9] have suggested filter techniques for selecting relevant attribute form synthetic data set and studying the performance of the techniques with reduced feature subset.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Ensemble Model with Feature Selection Technique for Classification of Chronic Kidney Disease

2019

IJEAT

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Healthcare diagnosis system is very important and critical task in medical science for doctors and medical students. Chronic kidney disease is a very serious and dangerous problem which is directly related to the human life. In this research work, we have used data mining and feature selection technique to develop the robust and computationally efficient model for classifying chronic and non chronic kidney disease. An ensemble model is constructing through combination of two more similar types of trained model which helps to improve the performance. Feature selection is frequently used in machine learning area to raise a model with a few numbers of features which increase the performance of classification accuracy. The proposed feature selection techniques principle of Genetic Search (GS) and Greedy Stepwise Search (GSW). This proposed technique called GS-NB utilizes a pursuit methodology which is embedded in the Genetic Algorithm to select the features based on natural selection, the procedure that drives biological evolution. Then proposed technique called GSW-NB utilizes a search strategy that is included in the Greedy Stepwise to search the relevant feature based on problem solving heuristic for settling the locally ideal decision at each stage. The performance of suggested technique were estimated on Chronic Kidney Disease (CKD) classification problems and compared with proposed feature selection method. The classification techniques namely the Single Rule Classification (SRC), Conditional Inference Tree (CIT) and their ensemble model (SRC, CIT) have used for classification of CKD. The proposed ensemble model have used stacking learning technique which combines multiple classifiers, hence we can improve the performance of classifiers. The classifier performance is measured with observed accuracy, sensitivity and specificity. The experimental results demonstrated that the ensemble model (SRC, CIT) with GS-NB and GSW-NB can recognized CKD better than existing model. The proposed model can be beneficial and useful in medical science for identifying and diagnosis of chronic kidney disease.

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Chronic Kidney Disease analysis using data mining classification techniques

Cited by 116 publications

References 29 publications

A Model for the Mental Health Evaluation of Skilled Workers in Technology-Based Companies

A Model for the Mental Health Evaluation of Skilled Workers in Technology-Based Companies

Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines

A Proposed Ensemble Model with Feature Selection Technique for Classification of Chronic Kidney Disease

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