Abstract:
Data mining is the processes of extraction useful pattern and model from huge dataset. These
model and pattern have an effective role in decision making task. Data mining basically depends
on quality of data. Raw data usually have missing value, noise data, incomplete data, inconsistent
data and outlier data. So any raw data should preprocess before mining. The existing problems
The Doctor and Nurse Give treatment with guess without developing prototype. Data preprocess
is the main task of data mining. This research is done using WEKA software because it can
understand large data set and have several data preprocessing technique like cleaning,
integration, transformation, discretization and reduction. This research study uses different
algorithm to measure the efficiency of data and show detail description of data preprocessing
technique which are used for data mining. The quality of data can be measure in different ways
like confusion matrix, Roc curve and cross validation of the given dataset can test and train and
can generate actual and predictive value. In this research the five mostly used classification
technique such as Naïve Bayes(N.B), Bayes net(B.N), J48, Random forest(RF) and OneR is used
to evaluating for Asthma prediction using Asthma dataset. The experiment result shows that the
classification Accuracy using N.B(83.9%), Bayes net(84.95%), J48(85.29%), Random
forest(82.49%) and OneR (84.95%
