Authors : Nor Syuhada Che Khalid, Burhanuddin Mohd. Aboobaider, Nuzulha Khilwani Ibrahim, Zahriah Sahri and Mohd. Khanapi Abd. Ghani
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.
Nor Syuhada Che Khalid, Burhanuddin Mohd. Aboobaider, Nuzulha Khilwani Ibrahim, Zahriah Sahri and Mohd. Khanapi Abd. Ghani, 2017. Data Mining Variables and Features Selection for Malaysia Blood Donor’s Preference Using Correlation Technique. Journal of Engineering and Applied Sciences, 12: 3638-3643.