Abstract
Chronic diseases are one of the biggest threats to human life. It is clinically significant to predict the chronic disease prior to diagnosis time and take effective therapy as early as possible. In this work, we use problem transform methods to convert the chronic diseases prediction into a multi-label classification problem and propose a novel convolutional neural network (CNN) architecture named GroupNet to solve the multi-label chronic disease classification problem. Binary Relevance (BR) and Label Powerset (LP) methods are adopted to transform multiple chronic disease labels. We present the correlated loss as the loss function used in the GroupNet, which integrates the correlation coefficient between different diseases. The experiments are conducted on the physical examination datasets collected from a local medical center. In the experiments, we compare GroupNet with other methods and models. GroupNet outperforms others and achieves the best accuracy of 81.13%.
Highlights
Chronic diseases account for a majority of healthcare costs and they have been the main cause of mortality in the worldwide (Lehnert et al, 2011; Shanthi et al, 2015)
It is clear that the LPGroupNet obtains the best performance when the learning rate is 0.002 according to Figure 6B
The results demonstrate that correlated loss (CL) works better than fatty liver (FL) and CE based on the binary relevance (BR)-GroupNet in this work, which increases approximately 0.6% on all metrics
Summary
Chronic diseases account for a majority of healthcare costs and they have been the main cause of mortality in the worldwide (Lehnert et al, 2011; Shanthi et al, 2015). With the development of preventive medicine, it is very important to predict chronic diseases as early as possible. It is difficult for clinicians to make useful diagnosis in advance, because the pathogeny of chronic disease is fugacious and complex. Clinicians firstly form the diagnostic results of chronic disease according to the physical examination records based on their expertise and experience. With more and more physical examination records produced, clinicians would have difficulty forming accurate diagnosis in limited time. Artificial intelligence technology has brought enormous reform in medical domain, and it can help doctor diagnose by forming the diagnostic results automatically based on the prediction models. A symptom is always associated with multiple chronic diseases based on the physical examination records. The diagnosis or prediction of multiple chronic diseases could be transformed into a multi-label classification problem
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