Genetic Algorithm Based Model for Early Detection of Cancer

Cancers are a large everyday of diseases that involve uncharacteristic cell growth with the potential to spread to other parts of the body. A cancer syndrome in any of its forms represents a major cause of death universal. In cancer diagnosis, classification of different cancer types is of the greatest significance. Accuracy for prediction of various cancer types gives better handling and minimization of toxicity on patients. Consequently, creating methodologies that can effectively differentiate between cancer subtypes is essential. There are various conventional methods to predict early cancer still needsimprovements to solve the issues and challenges like accuracy, sparsity and Scalability. Hence, this paper presents a new methodology based on genetic algorithm to classify and predict Human cancer diseases tested using real-world cancer datasets. This methodology combines both genetic algorithm and neural network to classify and predict cancer earlier. The proposed system is evaluated by classifying and prediction cancer diseases in various cancer datasets and evaluation measures. The results are compared with latest methods for performance benchmark.