Comparative Analysis of Classification Methods Based on Medical Datasets

Abstract

Data mining can be used to predict the disease for the medical database to authenticate hidden strategies. It normally extracts the useful knowledge domain from medical datasets. Various classifi- cation and feature selection methods are available to predict diseases. This paper examines on five different diseases which include the BCD, PID, HS and CID databases. It normally considered the existing related work to search out important knowledge domains in this field and explained different methods such as ELM, CNN, SVM, PSOA and KNN used in disease prediction. The main discus- sion about the classification methods is used to improve the accuracy rate, recall, and precision rate. Data mining methods are very efficient for the job of classification. The current classification methods are used for classifying the diseases (Medical Databases). Various classification tech- niques have experimented on different databases of the UCI machine learning repository site for managing medical database classification. The simulation result depicts that various methods can attain better performance and compare to the results of other classifiers.