Performance comparison of classification algorithms for diagnosing chronic kidney disease

• Main Authors: De Guia, Justin D., Concepcion, Ronnie S., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Kidneys—Diseases—Diagnosis—Automation; Perceptrons; Machine learning; Nearest neighbor analysis (Statistics); Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2675
• Institution: De La Salle University

Chronic kidney disease (CKD) is one of the diseases with high mortality rate. It is a disease resulted from kidney function loss over a long period of time. The disease shows no symptoms during initial stage. When left not medicated, a person may suffer from other complications such as high blood pressure, anemia, malnutrition, increased risk of cardiovascular disease, cognitive impairment and impaired physical function. Automated diagnosis by using classification algorithms has been an interest of researchers. In this study, six machine learning algorithms were used for classification and its prediction performance was compared based on training time and F1 score, with and without hypertuning the parameters. Of all the six algorithms, KNN has the best F1 score of 0.992248 and minimal training time of 46.999ms. The performance of decision trees was improved with hypertuning, having a F1 score from 0.96 to 0.99. Overall, machine learning algorithms are significant tool to assess chronic kidney disease. © 2019 IEEE.