Evaluating the performance of multilayer perceptron algorithm for tuberculosis disease Raman data

In spite of developments in various molecular approaches, major challenges remain in rapidly diagnosing infectious diseases triggered by bacteria. Identification of such causative pathogens at an earlier stage and with an acceptable degree of sensitivity and specificity would play a major role in initiating proper treatment. In this study the performance of multilayer perceptron (MLP) algorithm on the Raman Spectroscopic data of tuberculosis disease have been evaluated. Blood sera samples of TB positive (active patients), TB negative (recovered) and control (healthy) are analyzed in current study. Classifications among the data sets are based on the differences/similarities in Raman peak intensity. The analysis has been carried out by using MLP, a class of artificial neural network algorithm. The results of these classifications are built on intensities of most dominated Raman peaks i.e. 1001, 1152, 1282, 1430, 1475, and 1690cm−1. These Raman shifts are attributed to biomolecules concentration such as phenylalanine, β-carotene, amide III and C=O of amide-I band of protein etc. The performance of the proposed model is evaluated using 5-fold cross validation method for the data sets i.e. control vs. TB positive, control vs. TB negative and TB positive vs. TB negative. The sensitivity and specificity predicted by the model is in the range of 62-92% and 81-88%, respectively. Once trained on known data set, Raman spectroscopy together with statistical algorithms can provide real time prediction for unknown samples.