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Research Article Open Access
In Machine Learning, Decision tree is the mostly used classifier for predictive Modeling. The C4.5 classifier suffers from overfitting; poor attribute split technique, inability to handle continuous valued and missing valued attributes with high learning cost. Among all, overfitting and split attribute has high impact on the accuracies of prediction. The Efficient Back-track pruning algorithm is introduced here to overcome the drawback of overfitting. The proposed concept is implemented and evaluated with the UCI Machine Learning Hungarian database. This database having 294 records with fourteen attributes were used for forecasting the heart disease and relevant accuracies were measured. This implementation shows that the proposed Back-track pruned algorithm is efficient when compared with existing C4.5 algorithm, which is more suitable for the application of large amounts of healthcare data. Its accuracy has been greatly improved in line with the practical Health care Historical data. The result obtained proves that the performance of Back-track pruned C4.5 algorithm is better than C4.5 algorithm.
Data mining, Heart disease, Decision Tree, C4.5 algorithm, Overfitting, Back-track pruning, #