Hybrid Machine Learning Based Approach to Reduce the Features for Prediction of Long-Term Renal Ailment
Keywords:
Dimensionality Reduction, Logistic Regression, Random Forest, Support Vector Machines, Extended Gradient Boosting Trees, Machine Learning, Chronic Kidney DiseaseAbstract
The most intensifying communal health problem is chronic kidney disease (CKD) caused by various conditions that reduce the efficiency of the kidneys which results in other health complications, that ultimately lead to the demise of the affected individual. This paper describes an experiment that uses a novel approach to fill missing values and combines related attributes based on domain knowledge and it is evaluated by 4 different Machine Learning (ML) classification algorithms – Logistic Regression, an Extended Gradient Boosting trees, Artificial Neural Networks (ANN), and Support Vector Machine (SVM). Among these four algorithms, ANN and XG-Boost provide the better result of 0.97 F1- Score. The standard related data is taken from a repository called UCI machine learning, which has 400 individual data, 250 were reported to have CKD.
Downloads
References
Zhang Q.L., Rothenbacher D., Prevalence of chronic kidney disease in population-based studies: a systematic review, BMC public health, 2008. 8(1): p. 117.
Baumgarten M., Gehr T., Chronic kidney disease: detection and evaluation, American family physician, 2011. 84(10): p. 1138.
Moyer V. A., US Preventive Services Task Force*, screening for chronic kidney disease: US Preventive Services Task Force recommendation statement. Annals of internal medicine, 2012. 157(8): p.567-570
Remuzzi G., Ruggenenti P., Perico N., Chronic renal diseases: renoprotective benefits of renin-angiotensin system inhibition, Annals of internal medicine, 2002. 136(8): p.604-615.
Patil, P. ., D. D. . Waghole, D. V. . Deshpande, and D. M. . Karykarte. “Sectoring Method for Improving Various QoS Parameters of Wireless Sensor Networks to Improve Lifespan of the Network”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, June 2022, pp. 37-43, doi:10.17762/ijritcc.v10i6.5622.
Plantinga Laura C., Boulware L.E., Coresh J., Stevens L.A., Miller E.R., Saran R., Messer K.L., Levey S.A., and Powe N.R., Patient awareness of chronic kidney disease: trends and predictors, Archives of internal medicine , 2008. 168(20): p.2268-2275.
Plantinga L. C., Tuot D. S., Powe N. R., Awareness of chronic kidney disease among patients and providers”, Advances in chronic kidney disease, 2010. 17(3): p. 225–236.
Chaudhary, D. S. . (2022). Analysis of Concept of Big Data Process, Strategies, Adoption and Implementation. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(1), 05–08. https://doi.org/10.17762/ijfrcsce.v8i1.2065
Peralta C.A., Whooley M.A., Ix J.H., Michael G., Kidney Function and Systolic Blood Pressure New Insights From Cystatin C: Data from the Heart and Soul Study, American Journal of Hypertension, 2006. 19(9): p. 939–946.
Bigotte Vieira M., Neves J.S., Leitão L., Baptista R.B., Magriço R., Dias C.V., Oliveira A., Carvalho D., Mc Causland F.R., Impaired Fasting Glucose and Chronic Kidney Disease, Albuminuria, or Worsening Kidney Function: A Secondary Analysis of SPRINT, The Journal of Clinical Endocrinology & Metabolism, 2019. 104(9): p. 4024–4032.
Jha V., Garcia-Garcia G., Iseki K., Li Z., Naicker S., Plattner B., Saran R., Wang A.Y.M., Yang C.W., Chronic kidney disease: global dimension and perspectives Lancet. , 2013: p. 260-272.
Afsar B., Turkmen K., Covic A., Kanbay M., An Update on Coronary Artery Disease and Chronic Kidney Disease, International Journal of Nephrology, 2014.
Shankar, M., Narasimhappa S., Madhura N.S., Urinary tract infection in chronic kidney disease population: a clinical observational study. Cureus, 2021. 13(1).
UCI Machine Learning Repository Pima Indians Diabetes Data Set- online, https://archive.ics.uci.edu › support › diabetes.
Dubey A., A classification of ckd cases using multivariate k-means clustering, International Journal of Scientific and Research Publications, 2015. 5(8): p.1-5.
Chronic Kidney Disease Dataset (URL ‘http://archive.ics.uci.edu/ml/datasets/Chronic Kidney Disease’
Salekin A., Stankovic J., Detection of chronic kidney disease and selecting important predictive attributes, In 2016 IEEE International Conference on Healthcare Informatics (ICHI), 2016: p. 262-270.
The New York Times health guide for swelling, accessed: 2016-3-11 http://www.nytimes.com/health/guides/symptoms/swelling/overview.html.
Doğruyol Başar M., Aydin A.K.A.N., Chronic Kidney Disease Prediction with Reduced Individual Classifiers, Istanbul University, Istanbul, Turkey. Electrica, 2018, 18(2): p. 249-255,
Ahmed Cherif Megri, Sameer Hamoush, Ismail Zayd Megri, Yao Yu. (2021). Advanced Manufacturing Online STEM Education Pipeline for Early-College and High School Students. Journal of Online Engineering Education, 12(2), 01–06. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/47
Kaur G., Sharma A., Predict Chronic Kidney Disease Using Data Mining Algorithms In Hadoop, Amritsar College of Engineering and Technology, Amritsar, India. International Journal of Advances in Electronics and Computer Science, Apr.-2018. 5(4), ISSN: 2393-2835.
Pitchumani Angayarkanni S., Predictive Analytics of Chronic Kidney Disease using Machine Learning Algorithm, Lady Doak College, Madurai, Tamil Nadu, India. International Journal of Recent Technology and Engineering, July 2019. 8 (2), ISSN: 2277-3878, DOI: 10.35940/ijrte.B1727.078219
Kumar C.S., Thangaraju P., Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines, Al, Bharathidasan University, Tiruchirappalli, Tamil Nadu, India. International Journal of Engineering and Advanced Technology, 2019. 8(6), ISSN: 2249 – 8958, DOI: 10.35940/ijeat.F9377.088619.
Revathy S., Bharathi B., Jeyanthi P., Ramesh M., Chronic Kidney Disease Prediction using Machine Learning Models, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India. International Journal of Engineering and Advanced Technology, October 2019. 9 (1), ISSN: 2249 – 8958, DOI: 10.35940/ijeat.A2213.109119
Almasoud M., Ward T.E., Detection of Chronic Kidney Disease using Machine Learning Algorithms with Least Number of Predictors, College of Computer and Information Science, Princess Nourah bint Abdul Rahman University, Riyadh, Saudi Arabia. International Journal of Advanced Computer Science and Applications, 2019. 10(8).
Hermina, J. ., Karpagam, N. S. ., Deepika, P. ., Jeslet, D. S. ., & Komarasamy, D. (2022). A Novel Approach to Detect Social Distancing Among People in College Campus. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 153–158. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1823
Ramya S., Radha N., Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithms, PSGR Krishnammal College for Women, Coimbatore, India. International Journal of Innovative Research in Computer and Communication Engineering, 2016. 4(1).
Rubini L., Eswaran P., Generating comparative analysis of early-stage prediction of Chronic Kidney Disease, Alagappa University, Karaikudi, Tamil Nadu, India International OPEN ACCESS Journal of Modern Engineering Research, 2015. 5(7), ISSN: 2249–6645.
Pavithra N., Shanmugavadivu R., Efficient Early Risk Factor Analysis of Kidney Disorder Using Data mining Technique, PSG College of Arts & Science, Coimbatore, India International Journal of Innovative Research in Computer and Communication Engineering, 2017. 5(2), ISSN (Print): 2320-9798, DOI: 10.15680/IJIRCCE.2017. 0502084 1690.
Kumar M., Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm, Banaras Hindu University, Varanasi, India. International Journal of Computer Science and Mobile Computing, 2016. 5 (2): p. 24-33, ISSN 2320–088X.
Arora M., Sharma E.A., Chronic Kidney Disease Detection by Analyzing Medical Datasets in Weka, ACET, Amritsar, India. International Journal of Computer Application, 2016. 6(4).
Misir R., Mitra M., Samanta R.K.A., Reduced Set of Features for Chronic Kidney Disease Prediction, Vidyasagar University, Medinipur, West Bengal, India. Journal of Pathology Informatics, 2017. DOI: 10.4103/jpi.jpi_88_16.
Sharma S., Sharma V., Sharma A., Classification Techniques for Chronic Kidney Disease Diagnosis, University Of Jammu, Jammu, India.
Tekale S., Shingavi P., Wandhekar S., Chatorikar A., Prediction of Chronic Kidney Disease Using Machine Learning Algorithm, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering & Technology, Baramati, Pune, Maharashtra, India. International Journal of Advanced Research in Computer and Communication Engineering, October 2018. 7(10), ISSN (Print) 2319-5940, DOI: 10.17148/IJARCCE.2018.71021 92.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
