Machine Learning-Based Prediction System For Chronic Kidney Disease Using Associative Classification Technique

  • Authors

    • Zixian Wang
    • Jae Won Chung
    • Xilin Jiang
    • Yantong Cui
    • Muning Wang
    • Anqi Zheng
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.36.25377
  • Machine Learning, Classification Technique, Prediction System

  • Technological development, including machine learning, has a huge impact on health through an effective analysis of various chronic diseases for more accurate diagnosis and successful treatment. Kidney disease is a major chronic disease associated with aging, hypertension, and diabetes, affecting people 60 and over. Its major cause is the malfunctioning of the kidney in disposing toxins from the blood. This study analyzes chronic kidney disease using machine learning techniques based on a chronic kidney disease (CKD) dataset from the UCI machine learning data warehouse. CKD is detected using the Apriori association technique for 400 instances of chronic kidney patients with 10-fold-cross-validation testing, and the results are compared across a number of classification algorithms including ZeroR, OneR, naive Bayes, J48, and IBk (k-nearest-neighbor). The dataset is preprocessed by completing and normalizing missing data. The most relevant features are selected from the dataset for improved accuracy and reduced training time. The results for selected features of the dataset indicate 99% detection accuracy for CKD based on Apriori. The identified technique is further tested using four patient data samples to predict their CKD.

     

     

  • References

    1. [1] Dulhare UN & Ayesha M, “Extraction of action rules for chronic kidney disease using Naïve bayes classifierâ€, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), (2016), pp.1-5.

      [2] Gopika M, “Machine learning Approach of Chronic Kidney Disease Prediction using Clustering Techniqueâ€, International Journal of Innovative Research in Science, Engineering and Technology, Vol.6, No.7, (2017), pp.14488 14496.

      [3] The relationship between machine learning and datamining, (2018).

      [4] One R. An Introduction to Data Science, (2018).

      [5] Charleonnan A, Fufaung T, Niyomwong T, Chokchueypattanakit W, Suwannawach S & Ninchawee N, “Predictive analytics for chronic kidney disease using machine learning techniquesâ€, IEEE International Conference on Management and Innovation Technology (MITicon), (2016), pp.MIT-80.

      [6] Saltz JS & Stanton JM, An introduction to data science, SAGE Publications, (2017).

      [7] Ramya S & Radha N, “Diagnosis of chronic kidney disease using machine learning algorithmsâ€, International Journal of Innovative Research in Computer and Communication Engineering, Vol.4, No.1, (2016), pp.812-820.

      [8] Padmanaban KA & Parthiban G, “Applying Machine Learning Techniques for Predicting the Risk of Chronic Kidney Diseaseâ€, Indian Journal of Science and Technology, Vol.9, No.29, (2016), pp.1-5.

      [9] Iqbal F, Pallewatte AS & Wansapura JP, “Texture analysis of ultrasound images of chronic kidney disease. IEEE Seventeenth International Conference on Advances in ICT for Emerging Regions (ICTer), (2017), pp.1-5.

      [10] Kayaalp F, Basarslan MS & Polat K, “A hybrid classification example in describing chronic kidney diseaseâ€, IEEE Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), (2018), pp.1-4.

      [11] Wibawa MS, Maysanjaya IMD & Putra IMAW, “Boosted classifier and features selection for enhancing chronic kidney disease diagnoseâ€, IEEE 5th International Conference on Cyber and IT Service Management (CITSM), (2017), pp.1-6.

      [12] Wickramasinghe MPNM, Perera DM & Kahandawaarachchi KADCP, “Dietary prediction for patients with Chronic Kidney Disease (CKD) by considering blood potassium level using machine learning algorithmsâ€, IEEE Conference on Life Sciences (LSC), (2017), pp.300-303.

      [13] Patil PM, “Review on Prediction of Chronic Kidney Disease using Data Mining Techniquesâ€, International Journal of Computer Science and Mobile Computing, Vol.5, No.5, (2016), pp.135-141.

      [14] Jerlin L & Rubini DA, Chronic Kidney Disease, Retrieved from UCI Machine Learning Repository, (2018).

      [15] Sinha P & Sinha P, “Comparative study of chronic kidney disease prediction using KNN and SVMâ€, International Journal of Engineering Research and Technology, Vol.4, No.12, (2015), pp.608-612.

      [16] Khan SH, Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods, (2010), pp.1-40.

      [17] Al-Hyari AY, Ahmad MA & Majid AA, “Diagnosis and classification of chronic renal failure utilising intelligent data mining classifiersâ€, International Journal of Information Technology and Web Engineering (IJITWE), Vol.9, No.4, (2014), pp.1-12.

      [18] Jena L & Narendra KK, “Distributed data mining classification algorithms for prediction of chronic-kidney-diseaseâ€, International Journal of Emerging Research in Management &Technology, Vol.4, No.11, (2015), pp.110-118.

      [19] Vijayarani S & Dhayanand S, “Data mining classification algorithms for kidney disease predictionâ€, International Journal on Cybernetics and Informatics (IJCI), (2015), pp.13-25.

      [20] Kumar M, “Prediction of chronic kidney disease using random forest machine learning algorithmâ€, International Journal of Computer Science and Mobile Computing, Vol.5, No.2, (2016), pp.24-33.

      [21] Kumar G & Kumar K, “The use of multi-objective genetic algorithm based approach to create ensemble of ann for intrusion detectionâ€, International Journal of Intelligence Science, Vol.2, No.4, (2012), pp.115-127.

      [22] Kumar G & Kumar K, “A novel evaluation function for feature selection based upon information theory. IEEE 24th Canadian Conference on Electrical and Computer Engineering (CCECE), (2011), pp.000395-000399.

      [23] Kumar G, Kumar K & Sachdeva M, “The use of artificial intelligence based techniques for intrusion detection: a reviewâ€, Artificial Intelligence Review, Vol.34, No.4, (2010), pp.369-387.

  • Downloads

  • How to Cite

    Wang, Z., Won Chung, J., Jiang, X., Cui, Y., Wang, M., & Zheng, A. (2018). Machine Learning-Based Prediction System For Chronic Kidney Disease Using Associative Classification Technique. International Journal of Engineering & Technology, 7(4.36), 1161-1167. https://doi.org/10.14419/ijet.v7i4.36.25377