TY  - JOUR
PY  - 2019//
TI  - Gait biomarkers classification by combining assembled algorithms and deep learning: results of a local study
JO  - Computational and mathematical methods in medicine
A1  - Sánchez-DelaCruz, Eddy
A1  - Weber, Roberto
A1  - Biswal, R. R.
A1  - Mejía, Jose
A1  - Hernández-Chan, Gandhi
A1  - Gómez-Pozos, Heberto
SP  - 3515268
EP  - 3515268
VL  - 2019
IS  - 
N2  - Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training process. Machine learning has revolutionized every research sector, including health care, by providing precise and accurate decisions involving minimal human interventions through pattern recognition. This is emphasized in this research, which addresses the issue of "support for diabetic neuropathy (DN) recognition." DN is a disease that affects a large proportion of the global population. In this research, we have used gait biomarkers of subjects representing a particular sector of population located in southern Mexico to identify persons suffering from DN. To do this, we used a home-made body sensor network to capture raw data of the walking pattern of individuals with and without DN. The information was then processed using three sampling criteria and 23 assembled classifiers, in combination with a deep learning algorithm. The architecture of the best combination was chosen and reconfigured for better performance. The results revealed a highly acceptable classification with greater than 85% accuracy when using these combined approaches.<p />  <p>Language: en</p>
LA  - en
SN  - 1748-670X
UR  - http://dx.doi.org/10.1155/2019/3515268
ID  - ref1
ER  -