In Mexico cervical cancer is the second leading cause of death from malignant neoplasms in women, but this mortality rate has been reduced in recent years thanks to early detection programs as the pap smear test, which is aimed at finding pre-cancerous abnormalities in cells that cover the cervix. The pap smear test is an efficient medical test, but it presents problems at the moment of interpretation under the microscope, due to the large number of cells in the sample and others external factors. In order to solve this disadvantage, computational techniques are used to support the samples classification. In this research we propose to use assembled algorithms to construct a classifier. The database used is from Herlev University Hospital, the data were formulated as a binary classification problem. The results of the experiments (exhaustive search) show that using the combinations of algorithms Bagging+MultilayerPerceptron and AdaBoostM1+LMT is obtained a high percentage of correctly classified instances, 95.74%.
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.
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