Hybrid model for prediction of heart disease

Heart disease is a leading cause of death in the world. In order to drop its rate, effective and timely diagnosis of the disease is very essential. Numerous automated decision support systems have been developed for this purpose. In the present research, a predictive model consisting of two-level optimization is introduced, to save lives and cost via effective diagnosis of the disease. Level-1 optimization of the model first identifies parallelly an optimal proportion ( P opt ) for training and test sets for each dataset on parallel machine. Next, the best training set ( T best ) for P opt is again searched parallelly. On the other hand, level -2 optimization refines the rule set ( R ) generated by the Perfect Rule Induction by Sequential Method (PRISM) learner on T best employing parallel genetic algorithm. The experimental results obtained by the model over the heart disease datasets (collected from https://archive.ics.uci.edu/ml ) are compared and analysed with its base learner and four state-of-the-art learners, namely C4.5 (decision tree-based classifier), Naïve Bayes, neural network and support vector machine. The empirical outcomes (based on the top performance metrics—prediction accuracy, precision, recall, area under curve values, true positive and false positive rates) positively demonstrate that the new model is proficient in undertaking heart disease treatment. Importantly, the prediction accuracy of the presented hybrid model exceeds around 6% than that of the sequential GA-based hybrid model over almost all the chosen datasets. After all, the proposed system may work as an e - doctor to predict heart attack and assist clinicians to take precautionary steps.