A Framework for Diagnosing Kidney Disease in Diabetes Patients Using Classification Algorithms

• Published in: SN computer science Vol. 1; no. 2; p. 101
• Main Authors: Prasad, Kashi Sai, Reddy, N. Chandra Sekhar, Puneeth, B. N.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.03.2020; Springer Nature B.V
• Subjects: Accuracy; Advances in Computational Intelligence; Algorithms; Case studies; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data mining; Data Structures and Information Theory; Datasets; Decision trees; Diabetes; Error analysis; Evaluation; Experiments; Information Systems and Communication Service; Kidney diseases; Machine learning; Neural networks; Original Research; Paradigms and Applications; Pattern Recognition and Graphics; Performance evaluation; Root-mean-square errors; Software Engineering/Programming and Operating Systems; Vision; Naive Bayes; Classification; Random forest; Data mining; Kidney disease diagnosis; Decision table; J48
• ISSN: 2662-995X; 2661-8907
• DOI: 10.1007/s42979-020-0096-7

Data mining algorithms are widely used to extract latent trends or patterns from databases. They are used to predict unknown class labels in order to help in accurate decision making process. The machine learning techniques are very useful in applications of healthcare domain. In this domain, the techniques are employed to diagnose diseases and even predicting the probabilities related to health and other aspects. Since diabetes is one of the diseases that prevail in Indians now and it causes other diseases as well, in this paper we studied the kidney disease diagnosis in diabetes patients. Kidney disease dataset is used to explore four different data mining algorithms. They are known as Naive Bayes, decision table, J48 and random forest. The dataset is used to have both training and testing sets and produce prediction details. These algorithms are evaluated by using measures like accuracy, mean absolute error (MAE) and root-mean-squared error (RMSE). The results revealed that all the machine learning algorithms are able to predict the disease. However, they differ in accuracy levels. With tenfold cross-validation, it is understood that random forest showed the highest accuracy, while Naive Bayes showed least MAE and RMSE. We built prototype application to demonstrate proof of the concept. Our experimental results revealed that the proposed framework to evaluate machine learning algorithms is useful.