TY  - JOUR
T1  - Hybrid Decision Tree Fuzzy Rule Based Classifier for Heart Disease
Prediction Using Chaotic Cuckoo Search Algorithm
AU - Gobal, Jagadeesh AU - Narayan, Subhashini 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 23
SP  - 7358
EP  - 7366
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7358.7366
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7358.7366
KW  - Preprocessing
KW  -cuckoo search
KW  -fuzzy
KW  -decision tree
KW  -classification
KW  -outcomes
AB  - Heart disease is the primary cause of death in all over the world and one of the primary diseases in
developing countries. The disease identification and investigation needs a lot of prognosis and diagnosis with
the high cost. To eliminate these burdens, we need to predict this disease in early stages with the help of
machine learning techniques. The UCI repository heart data such as Cleveland, Hungarian dataset and
Switzerland dataset for disease prediction. The proposed method consists of three stages: initially inconsistent,
irrelevant and noisy data from the dataset using the fundamental fuzzy min. max. preprocessing, after that
orthogonal locality preservation projection technique is applied to the processed data to reduce the dimension.
At the second stage, chaotic cuckoo search algorithmetic approach is used for fitness evaluation, the
combinations of cuckoo search algorithm, fuzzy and decision tree classifier can create a hybrid class.
Information entropy algorithm will be sufficiently combined with cuckoo search algorithm achieves better
classification accuracy. The result of classification accuracy of other existing algorithms is respectively 0.89,
0.93, 0.91. Consequently, the obtained results have shown very promising outcomes for the diagnosis of heart
disease.
ER  -