Prediction of Chronic Diseases With Multi-Label Neural Network

Abstract

Chronic diseases have seriously affected human activities, especially in many developing countries and underdeveloped countries. The long duration of chronic diseases and the high cost of medical care have placed a huge economic burden on society and families. Meanwhile, chronic patients tend to have a variety of complications over time. So, it is difficult for doctors to find effective diagnosis and appropriate treatment. Machine learning techniques can integrate their heterogeneous data of various body indicators. Meanwhile, for chronic patients, multi-label learning methods can be used to help doctors identify the types of the chronic diseases. This paper proposes a novel multi-label neural network method (ML-NN) to predict the chronic diseases combining neural network and multi-label learning technology based on cross entropy lost function and backward propagation algorithm. Compared with 14 traditional multi-label learning methods on 10 chronic diseases and 19733 patients, the proposed method achieved a consistently best in 5 performance measurements. The results demonstrate the proposed method can effectively predict chronic diseases and assist doctors to diagnose and treat patients.

Keywords

• Chronic diseases prediction
• multi-label learning
• neural network