A SUPERVISED LEARNING TECHNIQUE TO PREDICT AORTIC STIFFNESS FROM THE DIGITAL VOLUME PULSE

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

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Prognosis of Diabetic Peripheral Neuropathy via Decomposed Digital Volume Pulse from the Fingertip

Entropy ◽

10.3390/e22070754 ◽

2020 ◽

Vol 22 (7) ◽

pp. 754

Author(s):

Hai-Cheng Wei ◽

Wen-Rui Hu ◽

Na Ta ◽

Ming-Xia Xiao ◽

Xiao-Jing Tang ◽

...

Keyword(s):

Logistic Regression ◽

Peripheral Neuropathy ◽

Diabetic Peripheral Neuropathy ◽

Protective Factor ◽

Binary Logistic Regression Analysis ◽

Diabetic Patients ◽

Frequency Noise ◽

Baseline Measurement ◽

Volume Pulse ◽

Digital Volume Pulse

Diabetic peripheral neuropathy (DPN) is a very common neurological disorder in diabetic patients. This study presents a new percussion-based index for predicting DPN by decomposing digital volume pulse (DVP) signals from the fingertip. In this study, 130 subjects (50 individuals 44 to 89 years of age without diabetes and 80 patients 37 to 86 years of age with type 2 diabetes) were enrolled. After baseline measurement and blood tests, 25 diabetic patients developed DPN within the following five years. After removing high-frequency noise in the original DVP signals, the decomposed DVP signals were used for percussion entropy index (PEIDVP) computation. Effects of risk factors on the incidence of DPN in diabetic patients within five years of follow-up were tested using binary logistic regression analysis, controlling for age, waist circumference, low-density lipoprotein cholesterol, and the new index. Multivariate analysis showed that patients who did not develop DPN in the five-year period had higher PEIDVP values than those with DPN, as determined by logistic regression model (PEIDVP: odds ratio 0.913, 95% CI 0.850 to 0.980). This study shows that PEIDVP can be a major protective factor in relation to the studied binary outcome (i.e., DPN or not in diabetic patients five years after baseline measurement).

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