Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

By Min Chen, Yixue Hao, Kai Hwang, Lu Wang, Lin Wang. IEEE Access, 2017

1,037 citations recorded by the source corpus.

With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013–2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.

Subfields: Artificial Intelligence, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7e6818390a82838ff3b3df1d7c680a14cd5b2e20

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Citations in the corpus

SupportNet: solving catastrophic forgetting in class incremental learning with support data (2018, methodology): “, 2015), and disease prediction (Mohanty et al., 2016; Chen et al., 2017) would face the serious problem of catastrophic forgetting.”
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Artificial Intelligence and the Implementation Challenge (2019, background): “Warning systems for disease outbreak [22] Identifying potential staff scheduling changes related to forecasted emergency room volumes [21] Producing a more accurate prediction of the likely outcome of a particular intervention [20] Decision support N/A d Use of robotics for operational tasks in…”
Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis (2020, background): “On the other hand, with the evolvement of big data in biomedical and medicinal service networks, accurate analysis of medical data becomes possible that could improve patient care [51]; if there is an unavailability of quality medical data, it could result in poor decision-making.”