10 research outputs found
Early hospital mortality prediction using vital signals
Early hospital mortality prediction is critical as intensivists strive to
make efficient medical decisions about the severely ill patients staying in
intensive care units. As a result, various methods have been developed to
address this problem based on clinical records. However, some of the laboratory
test results are time-consuming and need to be processed. In this paper, we
propose a novel method to predict mortality using features extracted from the
heart signals of patients within the first hour of ICU admission. In order to
predict the risk, quantitative features have been computed based on the heart
rate signals of ICU patients. Each signal is described in terms of 12
statistical and signal-based features. The extracted features are fed into
eight classifiers: decision tree, linear discriminant, logistic regression,
support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and
K-nearest neighborhood (K-NN). To derive insight into the performance of the
proposed method, several experiments have been conducted using the well-known
clinical dataset named Medical Information Mart for Intensive Care III
(MIMIC-III). The experimental results demonstrate the capability of the
proposed method in terms of precision, recall, F1-score, and area under the
receiver operating characteristic curve (AUC). The decision tree classifier
satisfies both accuracy and interpretability better than the other classifiers,
producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It
indicates that heart rate signals can be used for predicting mortality in
patients in the ICU, achieving a comparable performance with existing
predictions that rely on high dimensional features from clinical records which
need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE
2018 and published in Smart Health journa
Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets
Ensemble SVM for characterisation of crude oil viscosity
Abstract This paper develops ensemble machine learning model for the prediction of dead oil, saturated and undersaturated viscosities. Easily acquired field data have been used as the input parameters for the machine learning process. Different functional forms for each property have been considered in the simulation. Prediction performance of the ensemble model is better than the compared commonly used correlations based on the error statistical analysis. This work also gives insight into the reliability and performance of different functional forms that have been used in the literature to formulate these viscosities. As the improved predictions of viscosity are always craved for, the developed ensemble support vector regression models could potentially replace the empirical correlation for viscosity prediction
Imbalanced Classification using Genetically Optimized Cost Sensitive Classifiers
Abstract-Classification is one of the most researched problems in machine learning, since the 1960s a myriad of different techniques have been proposed. The purpose of a classification algorithm, also known as a 'classifier', is to identify what class, or category an observation belongs to. In many real-world scenarios, datasets tend to suffer from class imbalance, where the number of observations belonging to one class greatly outnumbers that of the observations belonging to other classes. Class imbalance has been shown to hinder the performance of classifiers, and several techniques have been developed to improve the performance of imbalanced classifiers. Using a cost matrix is one such technique for dealing with class imbalance, however it requires a matrix to be either pre-defined, or manually optimized. This paper proposes an approach for automatically generating optimized cost matrices using a genetic algorithm. The genetic algorithm can generate matrices for classification problems with any number of classes, and is easy to tailor towards specific use-cases. The proposed approach is compared against unoptimized classifiers and alternative cost matrix optimization techniques using a variety of datasets. In addition to this, storage system failure prediction datasets are provided by Seagate UK, the potential of these datasets is investigated