3 research outputs found
Heartbeat Anomaly Detection using Adversarial Oversampling
Cardiovascular diseases are one of the most common causes of death in the
world. Prevention, knowledge of previous cases in the family, and early
detection is the best strategy to reduce this fact. Different machine learning
approaches to automatic diagnostic are being proposed to this task. As in most
health problems, the imbalance between examples and classes is predominant in
this problem and affects the performance of the automated solution. In this
paper, we address the classification of heartbeats images in different
cardiovascular diseases. We propose a two-dimensional Convolutional Neural
Network for classification after using a InfoGAN architecture for generating
synthetic images to unbalanced classes. We call this proposal Adversarial
Oversampling and compare it with the classical oversampling methods as SMOTE,
ADASYN, and RandomOversampling. The results show that the proposed approach
improves the classifier performance for the minority classes without harming
the performance in the balanced classes
Case-based retrieval framework for gene expression data
© the authors, publisher and licensee Libertas academica Limited. Background: The process of retrieving similar cases in a case-based reasoning system is considered a big challenge for gene expression data sets. The huge number of gene expression values generated by microarray technology leads to complex data sets and similarity measures for high-dimensional data are problematic. Hence, gene expression similarity measurements require numerous machine-learning and data-mining techniques, such as feature selection and dimensionality reduction, to be incorporated into the retrieval process.Methods: This article proposes a case-based retrieval framework that uses a k-nearest-neighbor classifier with a weighted-feature-based similarity to retrieve previously treated patients based on their gene expression profiles. Results: The herein-proposed methodology is validated on several data sets: a childhood leukemia data set collected from The Children’s Hospital at Westmead, as well as the Colon cancer, the National Cancer Institute (NCI), and the Prostate cancer data sets. Results obtained by the proposed framework in retrieving patients of the data sets who are similar to new patients are as follows: 96% accuracy on the childhood leukemia data set, 95% on the NCI data set, 93% on the Colon cancer data set, and 98% on the Prostate cancer data set. Conclusion: The designed case-based retrieval framework is an appropriate choice for retrieving previous patients who are similar to a new patient, on the basis of their gene expression data, for better diagnosis and treatment of childhood leukemia. Moreover, this framework can be applied to other gene expression data sets using some or all of its steps
End-to-End Intelligent Framework for Rockfall Detection
Rockfall detection is a crucial procedure in the field of geology, which
helps to reduce the associated risks. Currently, geologists identify rockfall
events almost manually utilizing point cloud and imagery data obtained from
different caption devices such as Terrestrial Laser Scanner or digital cameras.
Multi-temporal comparison of the point clouds obtained with these techniques
requires a tedious visual inspection to identify rockfall events which implies
inaccuracies that depend on several factors such as human expertise and the
sensibility of the sensors. This paper addresses this issue and provides an
intelligent framework for rockfall event detection for any individual working
in the intersection of the geology domain and decision support systems. The
development of such an analysis framework poses significant research challenges
and justifies intensive experimental analysis. In particular, we propose an
intelligent system that utilizes multiple machine learning algorithms to detect
rockfall clusters of point cloud data. Due to the extremely imbalanced nature
of the problem, a plethora of state-of-the-art resampling techniques
accompanied by multiple models and feature selection procedures are being
investigated. Various machine learning pipeline combinations have been
benchmarked and compared applying well-known metrics to be incorporated into
our system. Specifically, we developed statistical and machine learning
techniques and applied them to analyze point cloud data extracted from
Terrestrial Laser Scanner in two distinct case studies, involving different
geological contexts: the basaltic cliff of Castellfollit de la Roca and the
conglomerate Montserrat Massif, both located in Spain. Our experimental data
suggest that some of the above-mentioned machine learning pipelines can be
utilized to detect rockfall incidents on mountain walls, with experimentally
proven accuracy