Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province

Background: In order to better assist medical professionals, this study aimed to develop and compare the performance of three models—a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model—to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province. Methodology/Principal findings: Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient’s outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity. Conclusions/Significance: Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC. Author summary: Worldwide, approximately 240 million individuals are infected with schistosomiasis, a parasitic neglected tropical disease that continues to be a significant cause of morbidity and mortality, especially in China. Effective tools that can accurately predict the prognosis of patients with advanced schistosomiasis would aid in the treatment and management of the disease. To this end, we constructed and compared the performance of three predictive models—an artificial neural network (ANN) model, a logistic regression (LR) model and a decision tree (DT) model—in their ability to predict the prognosis of patients with advanced schistosomiasis. We found that while all three models proved effective, the ANN model outperformed the LR and DT models in terms of AUC and sensitivity. Yet, to achieve the highest level of prediction accuracy and to better assist medical professionals, we recommend comparing the performance of the three predictive models to select the optimal one, which will be better than select a model at random. The findings of this study not only provide valuable information on the construction of effective predictive models for the prognosis of advanced schistosomiasis, but also offer new methodology for clinically determining patient diagnosis and prognosis.