Early Prediction of Non-communicable Diseases Using Soft Computing Methodology

Abstract

Even though non-communicable diseases (NCDs) are deadly diseases, the curing and survival rate of them can increase with early prediction. But identification of NCD in the early stage is difficult due to complex clinical attributes and genetic factors. This task can be simplified with the aid of data mining and soft computing techniques. Initially dataset is pre-processed to enhance data quality and then disease prediction model is developed with soft computing methods to identify the disease stage. Later association rules are generated after applying fuzzy clustering to predict the probability of getting the disease in the future and risk factors associated with it individual wise.