The prevalence of type 2 diabetes mellitus is increasing worldwide. Current methods of treating diabetes remain inadequate, and therefore, prevention with screening methods is the most appropriate process to reduce the burden of diabetes and its complications. We propose a new prognostic approach for type 2 diabetes mellitus based on electronic health records without using the current invasive techniques that are related to the disease (e.g. glucose level or glycated hemoglobin (HbA1c)). Our methodology is based on machine learning frameworks with data enrichment using temporal features. As as result our predictive model achieved an area under the receiver operating characteristics curve with a random forest classifier of 84.22 percent when including data information from 2009 to 2011 to predict diabetic patients in 2012, 83.19 percent when including temporal features, and 83.72 percent after applying temporal features and feature selection. We conclude that he pathology prediction is possible and efficient using the patient's progression information over the years and without using the invasive techniques that are currently used for type 2 diabetes mellitus classification.

This file was dowloaded from the institutional repository Brage NIH -brage.bibsys.no/nih Pimentel, A., Gomes, R., Olstad, B. H., Gamboa, H. (2015 (3) to analyze muscular fatigue through analysis of the frequency spectrum of the signal. We have developed an EMG tool that was incorporated in an existing web-based biosignal acquisition and processing framework. This tool can be used on a post-processing environment and provides not only frequency and time parameters, but also an automatic detection of starting and ending times for muscular voluntary contractions using a threshold-based algorithm with the inclusion of the Teager-Kaiser energy operator. The algorithm for the muscular voluntary contraction detection can also be reported after a real-time acquisition, in order to discard possible outliers and simultaneously compare activation times in different muscles. This tool covers all known applications and allows a careful and detailed analysis of the EMG signal for both clinicians and researchers. The detection algorithm works without user interference and is also user-independent. It manages to detect muscular activations in an interactive process. The user simply has to select the signal's time interval as input, and the outcomes are provided afterwards. RESEARCH HIGHLIGHTS• An interactive analysis tool for electromyographic (EMG) signals has been developed.• The tool gives the user control over signal processing algorithms, enabling human-computer interaction and provides visual information from the signals and processing results.• This tool differs from the existing ones due to the inclusion of an automatic detection algorithm for the muscular voluntary contraction-threshold-based method with the inclusion of the Teager-Keaser energy operator.• It allows a careful and detailed analysis of EMG signals both for clinicians and researchers.

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

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