Comparative Study of Chronic Kidney Disease Prediction Using Different Classification Techniques
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 34, Page: 195-203
2018
- 7Citations
- 16Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
There are many fields where data mining is effectively applicable like marketing retail e-business, which has made it noticeable to the alternate sectors also. One of such sector is healthcare. Healthcare sector has colossal data, but those data are not utilized in a productive way, which make it knowledge poor. Also they lack in a proficient tool which helps to discover the concealed relationship among the available data. This paper presents analysis on some data mining techniques particularly in chronic kidney diseases (CKDs). K-nearest neighbor (KNN), C4.5, support vector machine (SVM), and Naïve Bayes classification algorithm are applied on the same dataset. The experimental result implemented in Weka tool shows that the KNN algorithm gives more accurate result when contrasted with different algorithms.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85063296410&origin=inward; http://dx.doi.org/10.1007/978-981-10-8198-9_20; http://link.springer.com/10.1007/978-981-10-8198-9_20; http://link.springer.com/content/pdf/10.1007/978-981-10-8198-9_20; https://dx.doi.org/10.1007/978-981-10-8198-9_20; https://link.springer.com/chapter/10.1007/978-981-10-8198-9_20
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know