Predicting Cervical Cancer with Metaheuristic Optimizers for Training LSTM

Abstract

Disease prediction can be extremely helpful in saving people, especially when we are diagnosed with cancer. Cervical cancer, also known as uterine cancer, is the fourth most frequent cancer in women with an estimated 570,000 new cases in 2018 representing 6.6% of all female cancers. In accordance with World Health Organization (WHO), the mortality rate for cervical cancer reaches 90% in the underdeveloped nations and that the high mortality rate found in it could suffer a substantial reduction if there were: prevention, effective screening, treatment programs and early diagnosis. Artificial Neural Networks (ANN) has been helping to provide predictions in healthcare for several decades. Most research works utilize neural classifiers trained with backpropagation (BP) learning algorithm to achieve cancer diagnosis. the traditional BP algorithm has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In this work, we use a type of Recurrent Neural Network (RNN), known as Long Short-Term Memory (LSTM), whose main characteristic is the ability to store information in a series of temporal data. Instead of training the network with the backpropagation, the LSTM network was trained using five different metaheuristic algorithms: Cuckoo Search (CS), Genetic Algorithm (GA), Gravitational Search (GS), Gray Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). From results obtained can be observed that metaheuristic algorithms had performances above 96%.