A Model for predicting the need for orthopedics surgery by using data mining techniques

By expanding the use of computers in various aspects of people's lives, a huge amount of data is generated. Mostly this data contains valuable information. Data mining can enable us to extract required information and benefit from them. Data mining enables us to identify hidden patterns in data sets and use them for prediction. One of the areas that is faced with the massive production of data is the area of treatment. This study will focus in particular on orthopedics. This research is looking for using technology and data mining techniques from existing data in hospital's database to reach valuable information and predict possibility of breaks which   require orthopedics surgery. This may support doctors to make their decisions easier, faster and more accurately in serving patients.  This research is conducted by using the CRISP methodology. The result of this research shows that the combination of the CHAID algorithm and the Boosting cumulative amplified neural network can provide the desired accuracy in prediction of the need for orthopedics surgery.