Early Prediction of Non-communicable Diseases Using Soft Computing Methodology

  • Ravindar MogiliEmail author
  • G. NarsimhaEmail author
  • Konda SrinivasEmail author
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)


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.


Non-communicable diseases Early prediction SVM Neural network Fuzzy clustering Association rules Soft computing 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of CSEJyothishmathi Institute of Technology and ScienceKarimnagarIndia
  2. 2.Department of CSEJawaharlal Nehru Technological UniversityHyderabadIndia
  3. 3.Department of CSECMR Technical CampusHyderabadIndia

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