@article{MAKHILLJEAS2017121414644,
    title = {Data Mining Variables and Features Selection for Malaysia Blood Donor’s
Preference Using Correlation Technique},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {14},
    pages = {3638-3643},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.3638.3643},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.3638.3643},
    author = {Nor Syuhada Che,Burhanuddin Mohd.,Nuzulha Khilwani,Zahriah and},
    keywords = {Prediction,blood donors preferences,features arrangement,data mining,feature selection},
    abstract = {Dataset that was constructed from survey, interview or questionnaires forms may suggest about
Leading Features (LFs) from all Member Features (MFs) available and produce many sets of LF and MFs
combination. However, which LFs will take priority to extract important information approaches were not clearly
determine from past studies. Therefore, these study objectives are to introduce and analyze features
arrangement for prediction problem on blood donor’s preferences datasets to determine which LFs will take
priority to extract information through ranking and simplification. Artificial neural network will be used as
prediction algorithm for training, validating and testing. In the end, LFs analysis on features arrangement will
become useful to blood bank and health care community or organizer to arrange suitable strategy to attract
blood donors and contribute their blood to society, especially for everyday emergency and critical situation
for worst condition patients in surgeries, accidents and life threatening illnesses.}
    }