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Disease Prediction System Using Fuzzy C-Means Algorithm

S. Hemalatha, T. Kavitha, T. Bala Ramya


In today’s era, each and every human-being on earth depends on medical treatment and medicines. Every day we can hear some new diseases or new symptoms of the existing disease being discovered. But with the growing number of diseases and their symptoms, everyone cannot manage to be updated with it. To predict the diseases is one of the major challenges in past years and today also. . People tend to get suffered to or sometimes even die from certain diseases which could easily be cured, if those were known beforehand. This lack of knowledge sabotages the health of a person and can create deeper repercussions. This shows the importance of predicting the diseases early on the basis of available symptoms. Because of this it will become possible to cure the people from hazardous diseases which may lead the humans to death.

 The main objective of this paper is to predicting the disease of a patient based on the symptoms they enter using FCM or Fuzzy C Means algorithm. FCM is an unsupervised clustering algorithm, which allows one piece of data to belong to two or more clusters.


Clustering, FCM, Symptoms

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