Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Dec 2019
Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records.
Diabetes is responsible for considerable morbidity, healthcare utilisation and mortality in both developed and developing countries. Currently, methods of treating diabetes are inadequate and costly so prevention becomes an important step in reducing the burden of diabetes and its complications. Electronic health records (EHRs) for each individual or a population have become important tools in understanding developing trends of diseases. Using EHRs to predict the onset of diabetes could improve the quality and efficiency of medical care. In this paper, we apply a wide and deep learning model that combines the strength of a generalised linear model with various features and a deep feed-forward neural network to improve the prediction of the onset of type 2 diabetes mellitus (T2DM). ⋯ Our algorithm has further optimised the prediction of diabetes onset using a novel state-of-the-art machine learning algorithm: the wide and deep learning neural network architecture.
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Comput Methods Programs Biomed · Dec 2019
Developing an Indonesia's health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm.
Measuring health literacy becomes more important because its association with health status and healthcare outcomes. Studies have developed at least 133 measurement tools for health literacy. HLS-EU-Q47 is a questionnaire consisting of 12 sub-dimensions and 47 questions developed by the Europe Health Literacy Consortium. Many countries in Europe and Asia have used HLS-EU-Q47 as a tool for measuring health literacy in the general public. Indonesia has conducted general health literacy survey using HLS-EU-Q47 but finding the difficulties because of the time-consuming interview. A shorter version of HLS-EU-Q47 is needed to apply in health literacy researches in Indonesia. This paper reports the results of feature reduction to develop a short Indonesian version HLS-EU questionnaire and measures the accuracy of the model compared with other short form like HLS-EU-SQ16 or HLS-SF12. ⋯ A data mining technique using feature selection with combination of genetic algorithm and k-NN algorithm was applied to develop a short version questionnaire and proved to have better accuracy, as compared with the short version developed by traditional statistical technique.