Kidney failure prediction at an early stage using Machine Learning: A Comparative Study
Vijay Kumar
, Amrita ticku , Rachna Narula
Machine Learning, Data Mining, ANN, KNN, Decision Trees, Logistic Regression, Support Vector Machine, Data Preprocessing, Feature extraction.
Chronic kidney disease (CKD) is a medical complication of a person due to which the kidney can’t filter the blood due to which the body fills with extra water and waste products. It can lead to stroke, heart attack, heart failure, swelling of the feet and kidney failure, which can lead to death. The global health problem is growing rapidly as more and more people are being diagnosed with CKD. With advancing technology, as well as ongoing medical research, machine learning is being used in the healthcare sector to diagnose many diseases early. ML algorithms and decoding methods have been very useful in extracting, analyzing data and making predictions when a person is positive or negative about a disease based on the given data sets. ML algorithms and in-depth reading have been proven to be very true in detecting CKD early. Machine learning algorithms, Cat boost classifier, Support Vector Machine (SVM), DecisionTree (DT), RandomForest, KNN, ANN were studied and applied in this work to perform comparative analysis to shape a ML model which will accurately predict if a person is positive to CKD or not. This paper uses pre-data processing, including background and above-mentioned machine learning algorithms to build the most accurate model to accurately detect this disease CKD and perform a comparative research of various ML algorithms for prognosis of CKD .
"Kidney failure prediction at an early stage using Machine Learning: A Comparative Study", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 12, page no.182 - 192, December-2022, Available :https://ijsdr.org/papers/IJSDR2212029.pdf
Volume 7
Issue 12,
December-2022
Pages : 182 - 192
Paper Reg. ID: IJSDR_202946
Published Paper Id: IJSDR2212029
Downloads: 000347265
Research Area: Engineering
Country: New Delhi, New Delhi, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
