Prediction of Immunotherapy Success Rate: Particle Swarm Optimization Approach

Cancer is a disease that ranks second as a cause of death, one of which is warts caused by the Human Papilloma Virus. One of the warts treatment techniques that can be done is Immunotherapy, this method is another treatment that increases the human immune system. Naive Bayes Classifier or often called Bayesian Classification is a statistical classification method that can be used to predict the probability of membership of a class, Naive Bayes Classifier can be optimized by assigning a weight value to each attribute using the Particle Swarm Optimization method which aims to increase accuracy. Particle Swarm Optimization has advantages in selecting features and has superior performance in many optimization problems that can be solved more quickly, and the convergence rate tends to be more stable. The results of processing the dataset using this algorithm show a good accuracy value, which is 92.59% and AUC is 0.841. This research is useful in the field of health sciences, as information that needs to be followed up in testing the success rate of immunotherapy.