An Efficient Predictive Model for Probability of Genetic Diseases Transmission Using a Combined Model

In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivity and specificity. The results show that support vector machine algorithm outperformed the other two simple algorithms and the neural network and genetic algorithms offered better prediction at the end, while the proposed combined approach is developed using different parameters and outperformed the simple methods.