Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients

Abstract

Accurate disease prediction is an effective way to reduce medical costs. Due to the difference of eating habits and physical fitness of patients, the traditional disease prediction methods are facing an enormous challenge. How to find a reliable disease prediction method in the uncertain environment and improve the accuracy of prediction will be a valuable scientific problem. To obtain accurate prediction and help patients reduce medical costs, this paper introduces neighborhood rough set into multivariate variational mode decomposition, and proposes a new multi-attribute prediction approach. Firstly, to avoid the interference of redundant attributes, a multi-attribute reduction method based on neighborhood rough set is established. Then, to reduce the volatility and complexity of multi-attribute data in hybrid information system, a neighborhood rough set-based multivariable variational mode decomposition method is constructed. Subsequently, a predictor of extreme learning machine with kernel function, clearly defining the mapping relationship, is developed. Furthermore, Diebold–Mariano (DM) test and probability density distribution are used to evaluate the prediction results. Finally, 2041 random physical examination samples of potential gout patients are utilized to verify the effectiveness and practicability of the proposed approach. Experimental results show that the neighborhood rough set-based multi-attribute prediction approach has high accuracy and stability. Meanwhile, a new quantitative theory and method for chronic disease management decision-making can be provided in medical decision-making.