Evolutionary radial basis function network for gestational diabetes data analytics

Big data analytics in healthcare: a systematic literature review

“…Summary of findings of reviewed studies. (Chandola, Sukumar, and Schryver 2013); integrating techniques (Cheng, Kuo, and Zhou 2018); insights from sociodemographic data (Amirian et al 2017;Narayanan and Greco 2016); insurance claims (Chandola, Sukumar, and Schryver 2013); fraud identification (Chandola, Sukumar, and Schryver 2013); structural degradation modelling (Chehade and Liu 2019); macro-level phenomena (Cheng, Kuo, and Zhou 2018); public-health policy (Christensen et al 2018); social welfare policies (Wu et al 2016) Disease prediction Serious medical conditions (Chen et al 2017;Hadi et al 2019;Yasin and Rao 2018); gestational diabetes mellitus (Moreira et al 2018); diabetes (George, Chacko, and Kurien 2019;Gowsalya, Krushitha, and Valliyammai 2014); disease patterns (De Silva et al 2015); efficient risk profiling (Lin et al 2017); diagnostic frameworks (Babar et al 2016); prediction models (Manogaran et al 2018); prioritising individuals (Ozminkowski et al 2015;Sabharwal, Gupta, and Thirunavukkarasu 2016); personalised healthcare apps (Tseng et al 2017); patient monitoring (Christensen et al 2018;Sabharwal, Gupta, and Thirunavukkarasu 2016); disease-based monitoring systems (Bravo et al 2018); real-time assessment in m-Health (Bravo et al 2018); secure living environment for elderly (Jin et al, 2016) Strategy formulation BDA-based capabilities (Austin and Kusumoto 2016); investment in BDA (Sabharwal, Gupta, and Thirunavukkarasu 2016); efficient resource allocation (Gowsalya, Krushitha, and Valliyammai 2014); knowledge management …”

“…This theme captures the ways of predicting serious medical conditions in patients by using the efficient application of BDA, for example, in the prediction of diseases (Moreira et al 2018), the identification of disease patterns (De Silva et al 2015), and disease-based monitoring systems (Bravo et al 2018). Moreira et al (2018) identified that an artificial neural network-based approach is an excellent predictor for gestational diabetes mellitus.…”

“…This theme captures the ways of predicting serious medical conditions in patients by using the efficient application of BDA, for example, in the prediction of diseases (Moreira et al 2018), the identification of disease patterns (De Silva et al 2015), and disease-based monitoring systems (Bravo et al 2018). Moreira et al (2018) identified that an artificial neural network-based approach is an excellent predictor for gestational diabetes mellitus. (Gopal et al 2019); capability enhancement (Wang, Kung, and Byrd 2018); cloud-based infrastructure (Jindal et al 2018); hardware and software stacks (Koliogeorgi et al 2017); intelligent healthcare systems (Ma et al 2018); radial basis function network (Moreira et al 2018); wearable devices (Wu et al 2017); parallel execution of accelerated kernels (Koliogeorgi et al 2017); technology requirements (Jin et al, 2016); algorithms (Chen et al 2017;Forestiero and Papuzzo 2018;Shao et al 2016); architecture (Amirian et al 2017;Boudhir, Ben Ahmed, and Soumaya 2017); schemes (Jindal et al 2018) De Silva et al (2015) suggested that BDA can effectively identify disease patterns in patients.…”

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

“…In comparison to LR, the ANN model correctly predicted 70% of true positive diagnoses to the LR correct prediction of 56% of true positive diagnoses. Moreira et al propose application of the radial basis function network (RBFNetwork), an ANN technique, to identify possible cases of GDM in pregnant women and in result achieved 79% precision, and an F-measure of 0.79 [23]. Meant to support hospital management, the RBFNetwork is based on ANN in business intelligence.…”

Health Risk Prediction Using Big Medical Data - a Collaborative Filtering-Enhanced Deep Learning Approach

“…Some artificial intelligent techniques have been applied to the identification and prediction of diabetes disease. For example, Mario et al [14] proposed the modeling analysis of the radial basis function network (RBF Network) to identify possible cases of gestational diabetes that can lead to multiple risks for both the pregnant women and the fetus. The paper in [17] investigated the different roles of pixels in the image-level predictions of ConvNets when applied to the diabetic retinopathy (DR) screening dataset.…”