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An Approach to Detect Chronic Kidney Disease (CKD) by Removing Noisy and Inconsistent Values of UCI Dataset

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Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 348))

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Abstract

With the alarming rate of increase in chronic kidney disease (CKD) cases all over the world, researchers are trying to resolve it with state-of-the-art methods. It is evident that in a certain time period such disease gradually disrupts other organs functioning eventually causing death of patients. Early detection of CKD can diminish the chances of further damage to a great extent. Considering the UCI Machine Learning CKD dataset, this work attempts to present a more reliable approach, enabling handling of noisy data. However, CKD dataset contains noisy and inconsistent values, resulting in inaccurate prediction of CKD by using traditional machine learning algorithms. Therefore, this research presents an approach of handling noisy and inaccurate values of CKD dataset by employing a combination of deep neural network, statistical methods, Principal Component analysis (PCA), and “SMOTE”. Consequently, the refined CKD dataset coming out of the mentioned pre-processed methods is used in various machine learning methods. Our results showed that RF outperformed with 98.5% accuracy among Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Logistic Regression (LR) classifiers. Additionally, we found that the features such as serum-creatinine and blood urea exhibited their dominance in outcome prediction.

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Author information

Authors and Affiliations

  1. BGC Trust University Bangladesh, Biddyanagar, Chandanaish, Chattogram, Bangladesh

    Sabrina Jahan Maisha & Ety Biswangri

  2. University of Chittagong, Chittagong, Bangladesh

    Mohammad Shahadat Hossain

  3. Luleå University of Technology (LTU), Skellefteå, Sweden

    Karl Andersson

Authors
  1. Sabrina Jahan Maisha
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  2. Ety Biswangri
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  3. Mohammad Shahadat Hossain
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  4. Karl Andersson
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Corresponding author

Correspondence to Mohammad Shahadat Hossain .

Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Amity University Rajasthan, Jaipur, India

    Kanad Ray

  3. Surface Characterization Group, National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Faculty of Applied Sciences and Technology, Tun Hussein Onn University of Malaysia, Parit Raja, Johor, Malaysia

    Kavikumar Jacob

  5. Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Kek Sie Long

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Maisha, S.J., Biswangri, E., Hossain, M.S., Andersson, K. (2022). An Approach to Detect Chronic Kidney Disease (CKD) by Removing Noisy and Inconsistent Values of UCI Dataset. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_38

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