Predictions of Laryngeal Cancer Using Neural Network in Kerman Shafa Hospital

Introduction: One of the challenges facing medical science is the time and correct diagnosis of diseases. Particularly with regard to certain diseases such as the types of cancer, which are the leading causes of death worldwide, their early diagnosis has a significant impact on the control and treatment of this disease. The use of intelligent decision support systems with high precision can be a good way to reduce human error due to fatigue and lack of experience. Therefore, the present study tries to predict the disease by using data mining techniques and taking into account the variables that influence the prediction of laryngeal cancer. Material and Methods: This study is an analytical study. The data from the 249 cases referred to Shafa Hospital in Kerman in 2017 have been obtained. This study is based on the Crisp methodology and in the MATLAB software environment. First, in order to understand the laryngeal cancer, a review of related studies was conducted and interviewed by specialist physicians. Then, according to expert opinion, 24 variables were identified as effective factors in predicting laryngeal cancer. After clearing and preparing data, an artificial neural network model was used to predict the risk of laryngeal cancer. In the following, another model of the combination of the genetic algorithm and the neural network was created. Using genetic algorithm, 9 functional features of prediction of laryngeal cancer were determined from among the 24 selected variables, and artificial neural network was used to predict the risk of laryngeal cancer. Finally, the criteria for accuracy, specificity, and sensitivity were used to evaluate the two models. Results: The genetic algorithm reduced the complexity of the model by reducing the number of features from 24 to 9, but improved the average precision from 80% to 84%. Also, the model made with the characteristics selected by the genetic algorithm, increased the specificity and accuracy criteria by 13% and 8%, respectively. Conclusion: Combining the genetic algorithm with the neural network, in addition to improving the accuracy of prediction of laryngeal cancer, accelerates the diagnosis process, especially at the data collection stage, by reducing the number of characteristics required. Therefore, using this model as a smart decision system is suggested.