Open AccessA Comparative Analysis of the Performance of Multiple Data Mining Classification Approaches Using the Kn Fold Validation
Author(s) -
D. K. Girija,
Manish Kumar Varshney
Publication year - 2022
Publication title -
journal of pharmaceutical research international
Language(s) - English
Resource type - Journals
ISSN - 2456-9119
DOI - 10.9734/jpri/2022/v34i11b35542
Subject(s) - computer science , data mining , decision tree , correctness , naive bayes classifier , categorization , fold (higher order function) , data set , data quality , decision tree learning , machine learning , artificial intelligence , engineering , support vector machine , metric (unit) , operations management , programming language
In Healthcare the data is very large and sensitive. The data is mandatory to be handled very carefully without any negligence. A variety of data mining categorization approaches have been employed in the healthcare industry to assess the quality of services. On the basis of 150 patients' records, this study provides and evaluates the experience of implementing various data mining methodologies and procedures. Using data mining techniques, a new method for determining a product's correctness has emerged. The evaluation of performance on data mining classification by using a different algorithms like Decision Tree, Naïve Bayes, KNN, Radom Tree Set and Rule Model. Finally we tend to aim to contemplate the performance analysis of accuracy, sensitivity and specificity proportion to produce a result.