A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

Credit risk modeling: A comparative analysis of artificial and deep neural networks

Credit risk assessment plays a major role in the banks and financial institutions to prevent counterparty risk failure. One of the primary capabilities of a robust risk management system must be detecting the risks earlier, though many of the bank systems today lack this key capability which leads to further losses (MGI, 2017). In searching for an improved methodology to detect such credit risk and increasing the lacking capabilities earlier, a comparative analysis between Deep Neural Network (DNN) and machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN) and Artificial Neural Network (ANN) were conducted. The Deep Neural Network used in this study consists of six layers of neurons. Further, sampling techniques such as SMOTE, SVM-SMOTE, RUS, and All-KNN to make the imbalanced dataset a balanced one were also applied. Using supervised learning techniques, the proposed DNN model was able to achieve an accuracy of 82.18% with a ROC score of 0.706 using the RUS sampling technique. The All KNN sampling technique was capable of achieving the maximum true positives in two different models. Using the proposed approach, banks and credit check institutions can help prevent major losses occurring due to counterparty risk failure.credit riskdeep neural networkartificial neural networksupport vector machinessampling technique

Customer retention

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018The aim of this study is to model the probability of a customer to attrite/defect from a bank where, for example, the bank is not their preferred/primary bank for salary deposits. The termination of deposit inflow serves as the outcome parameter and the random forest modelling technique was used to predict the outcome, in which new data sources (transactional data) were explored to add predictive power. The conventional logistic regression modelling technique was used to benchmark the random forest’s results. It was found that the random forest model slightly overfit during the training process and loses predictive power during validation and out of training period data. The random forest model, however, remains predictive and performs better than logistic regression at a cut-off probability of 20%.MT 201

Incorporating feature ranking and evolutionary methods for the classification of high-dimensional DNA microarray gene expression data

Background: DNA microarray gene expression classification poses a challenging task to the machine learning domain. Typically, the dimensionality of gene expression data sets could go from several thousands to over 10,000 genes. A potential solution to this issue is using feature selection to reduce the dimensionality. Aim The aim of this paper is to investigate how we can use feature quality information to improve the precision of microarray gene expression classification tasks. Method: We propose two evolutionary machine learning models based on the eXtended Classifier System (XCS) and a typical feature selection methodology. The first one, which we call FS-XCS, uses feature selection for feature reduction purposes. The second model is GRD-XCS, which uses feature ranking to bias the rule discovery process of XCS. Results: The results indicate that the use of feature selection/ranking methods is essential for tackling high-dimensional classification tasks, such as microarray gene expression classification. However, the results also suggest that using feature ranking to bias the rule discovery process performs significantly better than using the feature reduction method. In other words, using feature quality information to develop a smarter learning procedure is more efficient than reducing the feature set. Conclusion: Our findings have shown that extracting feature quality information can assist the learning process and improve classification accuracy. On the other hand, relying exclusively on the feature quality information might potentially decrease the classification performance (e.g., using feature reduction). Therefore, we recommend a hybrid approach that uses feature quality information to direct the learning process by highlighting the more informative features, but at the same time not restricting the learning process to explore other features

Designing a streaming algorithm for outlier detection in data mining—an incrementa approach

To design an algorithm for detecting outliers over streaming data has become an important task in many common applications, arising in areas such as fraud detections, network analysis, environment monitoring and so forth. Due to the fact that real-time data may arrive in the form of streams rather than batches, properties such as concept drift, temporal context, transiency, and uncertainty need to be considered. In addition, data processing needs to be incremental with limited memory resource, and scalable. These facts create big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in an incremental fashion, especially in the streaming environment. To address these problems, we first propose C_KDE_WR, which uses sliding window and kernel function to process the streaming data online, and reports its results demonstrating high throughput on handling real-time streaming data, implemented in a CUDA framework on Graphics Processing Unit (GPU). We also present another algorithm, C_LOF, based on a very popular and effective outlier detection algorithm called Local Outlier Factor (LOF) which unfortunately works only on batched data. Using a novel incremental approach that compensates the drawback of high complexity in LOF, we show how to implement it in a streaming context and to obtain results in a timely manner. Like C_KDE_WR, C_LOF also employs sliding-window and statistical-summary to help making decision based on the data in the current window. It also addresses all those challenges of streaming data as addressed in C_KDE_WR. In addition, we report the comparative evaluation on the accuracy of C_KDE_WR with the state-of-the-art SOD_GPU using Precision, Recall and F-score metrics. Furthermore, a t-test is also performed to demonstrate the significance of the improvement. We further report the testing results of C_LOF on different parameter settings and drew ROC and PR curve with their area under the curve (AUC) and Average Precision (AP) values calculated respectively. Experimental results show that C_LOF can overcome the masquerading problem, which often exists in outlier detection on streaming data. We provide complexity analysis and report experiment results on the accuracy of both C_KDE_WR and C_LOF algorithms in order to evaluate their effectiveness as well as their efficiencies

Position Falsification Detection in VANET with Consecutive BSM Approach using Machine Learning Algorithm

Vehicular ad-hoc network (VANET) is an emerging technology for vehicle-to-vehicle communication vital for reducing road accidents and traffic congestion in an Intelligent Transportation System (ITS). VANET communication is vulnerable to various attacks and cryptographic techniques are used for message integrity and authentication of vehicles in order to ensure security and privacy for vehicular communications. However, if there is an inside attacker additional measures are necessary to ensure the correctness of the transmitted data. A basic safety message (BSM) is broadcasted by each vehicle in the network periodically to transmit its status. Position falsification is an attack where the attacker broadcasts a false BSM position, leading to congestion or even accidents. It becomes imperative to detect and identify the attacker to ensure safety in the network. Although many trust-based models are researched in the past, this research proposes a feasible and efficient data-centric approach to detect malicious behavior, using machine learning (ML) algorithms.The proposed Machine Learning based misbehavior detection system utilizes labelled dataset called Vehicular Reference Misbehavior Dataset (VeReMi). VeReMi dataset offers five different types of position falsification attacks with different vehicle and attacker densities. This ML-based model uses two consecutive BSM approach to detect these attacks. Model classification on the Road-side Unit detects and could revoke malicious nodes from the network, reducing computational overhead on vehicles