Browsing by Author "Vaisla, K.S."

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    An Improved Method for Disease Prediction Using Fuzzy Approach
    (Institute of Electrical and Electronics Engineers Inc., 2015) Chetty, N.; Vaisla, K.S.; Patil, N.
    Data mining is a process of extracting useful information from the huge amount of data. Data Mining has great scope in the field of medicine. This article deals with the working on PIMA and Liver-disorder datasets. Many researchers have proposed the use of K-nearest neighbor (KNN) algorithm for diabetes disease prediction. Some researchers have proposed a different approach by using K-means clustering for preprocessing and then using KNN for classification. These approaches resulted in poor classification accuracy or prediction. In our work we proposed and developed two different methods first one is fuzzy c-means clustering algorithm followed by a KNN classifier and second one is fuzzy c-means clustering algorithm followed by fuzzy KNN classifier to improve the accuracy of classification. We are successful in obtaining the better results than the existing methods for the given datasets. Our second approach produced better result than the first one. Classification is carried out using ten folds cross-validation technique. © 2015 IEEE.
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    An Improved Method for Disease Prediction Using Fuzzy Approach
    (2015) Chetty, N.; Vaisla, K.S.; Patil, N.
    Data mining is a process of extracting useful information from the huge amount of data. Data Mining has great scope in the field of medicine. This article deals with the working on PIMA and Liver-disorder datasets. Many researchers have proposed the use of K-nearest neighbor (KNN) algorithm for diabetes disease prediction. Some researchers have proposed a different approach by using K-means clustering for preprocessing and then using KNN for classification. These approaches resulted in poor classification accuracy or prediction. In our work we proposed and developed two different methods first one is fuzzy c-means clustering algorithm followed by a KNN classifier and second one is fuzzy c-means clustering algorithm followed by fuzzy KNN classifier to improve the accuracy of classification. We are successful in obtaining the better results than the existing methods for the given datasets. Our second approach produced better result than the first one. Classification is carried out using ten folds cross-validation technique. � 2015 IEEE.