Analysis and comparison of data mining tools for back and neck pain prediction

Abstract EN

Backpacks are commonly used among schoolchildren, adolescents and adults for daily carrying personal belongings, from home to school, the musculoskeletal problems associated with backpack have become an increasing concern with school children, subsequently increases the risk of developing chronic back pain in adulthood.

A realistic a pre-collected data set of students on back and neck pain and school bag weight was used, with 11 attributes, consists of 409 primary school students (204 male and 205 female), age range between (8-16) years.

Data mining techniques have been used for analysis to predict back and neck pain, based on the Naïve Bayes algorithm by using three data mining tools Weka, orange and rapid miner.

The main observation is the Rapid miner Recall is 0.93 %, while Weka and orange recall are 0.71 %, 0.75.

Also, this study indicates that the rapid miner, Weka, orange precision are 0.74 %, 0.75 %, 0.73 %.

This study showed that, the highest recall was achieved by Rapid miner tool, while the less achievement by Weka and orange.

Whereas, the precision for these tools almost the same.