An Adaptive Neuro-Fuzzy Inference System for Improving Data Quality in Disease Registries

The purpose of disease registries is to collect and analyze data related to specific diseases in terms of incidence and prevalence. Since the data is typically entered by wearable sensors and/or human caregivers, errors in the data fields are often inevitable. In this paper, we propose a new approach to improve data quality in disease registries based on (a) a semi-random combination of parameters and (b) a learning algorithm for detecting and signaling the loss of quality of the entered data. To implement the approach, we have developed a novel adaptive neuro-fuzzy inference system. It is applied to specific sections of the Tunisian Fanconi Anemia Registry with the aims of reducing false alarms and automatically adjusting the parameters of coefficients of the disease. Our experimental results indicate that both aims can be achieved and effectively lead to improved data quality in disease registries.