Comparative evaluation of the use of artificial neural networks for modelling the epidemiology of schistosomiasis mansoni

• Published in: Transactions of the Royal Society of Tropical Medicine and Hygiene 1996-07, Vol.90 (4), p.372-376
• Main Authors: Hammad, T.A., Abdel-Wahab, M.F., DeClaris, N., El-Sahly, A., El-Kady, N., Strickland, G.T.
• Format: Article
• Language: English
• Subjects: Adolescent; Age Factors; artificial neural networks; Biological and medical sciences; Child; Diseases caused by trematodes; Egypt - epidemiology; epidemiological modelling; Female; Forecasting; Helminthic diseases; Humans; Infectious diseases; Logistic Models; Male; Medical sciences; Neural Networks (Computer); Parasitic diseases; Risk Factors; Schistosoma mansoni; Schistosomiases; schistosomiasis; Schistosomiasis mansoni - epidemiology; Sensitivity and Specificity; Sex Factors; Tropical medicine
• ISSN: 0035-9203; 1878-3503
• DOI: 10.1016/S0035-9203(96)90509-X

There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal water and agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm, in which we built a suitable configuration of the network, using the first year's data, that optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably with logistic regression (NN sensitivity = 83% (95% confidence interval [CI] 78–88%) and positive predictive value (PPV) = 63% (95% CI 57–69%); logistic regression sensitivity = 66% (95% CI 60%–72%) and PPV = 59% (95% CI 53%–65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale control programme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.