• A. JENITA MARY
• K. JAYAVANI

In the past years, medical data mining has become a popular data mining subject. Researchers have proposed several tools and several methodologies for developing effective medical expert systems. The applications of data mining techniques to medical data extract patterns which are useful for diagnosis, prognoses and treatment of diseases. This extraction of patterns allows doctors and hospitals to be more effective and more efficient. The huge volume of data is the barrier in the detection of patterns. Feature selection techniques mainly used in data preprocessing for data mining. Classification task leads to reduction of the dimensionality of feature space, feature selection process is used for selecting large set of features. The proposed algorithm reduces the dimension efficiently by E-KNN algorithm which is derived from K-Nearest Neighbor Algorithm. The classification further improves by ACO, the swarm intelligence technique computes optimal solution based on E-KNN feature selection method applied on heart disease dataset for better prediction. The accuracy of classification for whole feature set and the reduced feature subset are compared.

Ant Colony Optimization-KNN, Feature Selection,Swarm intelligence,Heart disease.

"DIMENSION REDUCTION BASED ON ANT COLONY OPTIMIATION WITH EFFICIENT-KNN FOR PREDICTING OPTIMAL SOLUTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.26-31, June 2019, Available :http://www.jetir.org/papers/JETIR1906U41.pdf

"DIMENSION REDUCTION BASED ON ANT COLONY OPTIMIATION WITH EFFICIENT-KNN FOR PREDICTING OPTIMAL SOLUTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp26-31, June 2019, Available at : http://www.jetir.org/papers/JETIR1906U41.pdf