Avaliação da gravidade da malária utilizando técnicas de extração de características e redes neurais artificiais

Abstract

About half the world's population lives in malaria risk areas. Moreover, given the globalization of travel, these diseases that were once considered exotic and mostly tropical are increasingly found in hospital emergency rooms around the world. And often when it comes to experience in tropical diseases, expert opinion most of the time is not available or not accessible in a timely manner. The task of an accurate and efficient diagnosis of malaria, essential in medical practice, can become complex. And the complexity of this process increases as patients have non-specific symptoms with a large amount of data and inaccurate information involved. In this approach, Uzoka and colleagues (2011a), from clinical information of 30 Nigerian patients with confirmed malaria, used the Analytic Hierarchy Process method (AHP) and Fuzzy methodology to conduct the evaluation of the severity of malaria. The results obtained were compared with the diagnosis of medical experts. This paper develops a new methodology to evaluate the severity of malaria and compare with the techniques used by Uzoka and colleagues (2011a). For this purpose the data set used is the same of that study. The technique used is the Artificial Neural Networks (ANN). Are evaluated three architectures with different numbers of neurons in the hidden layer, two training methodologies (leave-one-out and 10-fold cross-validation) and three stopping criteria, namely: the root mean square error, early stop and regularization. In the first phase, we use the full database. Subsequently, the feature extraction methods are used: in the second stage, the Principal Component Analysis (PCA) and in the third stage, the Linear Discriminant Analysis (LDA). The best result obtained in the three phases, it was with the full database, using the criterion of regularization associated with the leave-one-out method, of 83.3%. And the best result obtained in (Uzoka, Osuji and Obot, 2011) was with the fuzzy network which revealed 80% accuracy