A theoretical framework for research on readmission risk prediction

On the one hand, predictive analytics is an important field of research in Information Systems (IS); however, research on predictive analytics in healthcare is still scarce in IS literature. One area where predictive analytics can be of great benefit is with regard to unplanned readmissions. While a number of studies on readmission prediction already exists in related research areas, there are few guidelines to date on how to conduct such analytics projects. To address this gap the paper presents the general process to develop empirical models by Shmueli and Koppius (2011) and extends this to the specific requirements of readmission risk prediction. Based on a systematic literature review, the resulting process defines important aspects of readmission prediction. It also structures relevant questions and tasks that need to be taken care of in this context. This extension of the guidelines by Shmueli and Koppius (2011) provides a best practice as well as a template that can be used in future studies on readmission risk prediction, thus allowing for more comparable results across various research fields

Restricting Supervised Learning: Feature Selection and Feature Space Partition

Many supervised learning problems are considered difficult to solve either because of the redundant features or because of the structural complexity of the generative function. Redundant features increase the learning noise and therefore decrease the prediction performance. Additionally, a number of problems in various applications such as bioinformatics or image processing, whose data are sampled in a high dimensional space, suffer the curse of dimensionality, and there are not enough observations to obtain good estimates. Therefore, it is necessary to reduce such features under consideration. Another issue of supervised learning is caused by the complexity of an unknown generative model. To obtain a low variance predictor, linear or other simple functions are normally suggested, but they usually result in high bias. Hence, a possible solution is to partition the feature space into multiple non-overlapping regions such that each region is simple enough to be classified easily. In this dissertation, we proposed several novel techniques for restricting supervised learning problems with respect to either feature selection or feature space partition. Among different feature selection methods, 1-norm regularization is advocated by many researchers because it incorporates feature selection as part of the learning process. We give special focus here on ranking problems because very little work has been done for ranking using L1 penalty. We present here a 1-norm support vector machine method to simultaneously find a linear ranking function and to perform feature subset selection in ranking problems. Additionally, because ranking is formulated as a classification task when pair-wise data are considered, it increases the computational complexity from linear to quadratic in terms of sample size. We also propose a convex hull reduction method to reduce this impact. The method was tested on one artificial data set and two benchmark real data sets, concrete compressive strength set and Abalone data set. Theoretically, by tuning the trade-off parameter between the 1-norm penalty and the empirical error, any desired size of feature subset could be achieved, but computing the whole solution path in terms of the trade-off parameter is extremely difficult. Therefore, using 1-norm regularization alone may not end up with a feature subset of small size. We propose a recursive feature selection method based on 1-norm regularization which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The selection process has a fast rate of convergence. We tested our method on an earthworm microarray data set and the empirical results demonstrate that the selected features (genes) have very competitive discriminative power. Feature space partition separates a complex learning problem into multiple non-overlapping simple sub-problems. It is normally implemented in a hierarchical fashion. Different from decision tree, a leaf node of this hierarchical structure does not represent a single decision, but represents a region (sub-problem) that is solvable with respect to linear functions or other simple functions. In our work, we incorporate domain knowledge in the feature space partition process. We consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. However it is not trivial to select the discrete or categorical attribute that maximally simplify the learning task. A naive approach exhaustively searches all the possible restructured problems. It is computationally prohibitive when the number of discrete or categorical attributes is large. We describe a metric to rank attributes according to their potential to reduce the uncertainty of a classification task. It is quantified as a conditional entropy achieved using a set of optimal classifiers, each of which is built for a sub-problem defined by the attribute under consideration. To avoid high computational cost, we approximate the solution by the expected minimum conditional entropy with respect to random projections. This approach was tested on three artificial data sets, three cheminformatics data sets, and two leukemia gene expression data sets. Empirical results demonstrate that our method is capable of selecting a proper discrete or categorical attribute to simplify the problem, i.e., the performance of the classifier built for the restructured problem always beats that of the original problem. Restricting supervised learning is always about building simple learning functions using a limited number of features. Top Selected Pair (TSP) method builds simple classifiers based on very few (for example, two) features with simple arithmetic calculation. However, traditional TSP method only deals with static data. In this dissertation, we propose classification methods for time series data that only depend on a few pairs of features. Based on the different comparison strategies, we developed the following approaches: TSP based on average, TSP based on trend, and TSP based on trend and absolute difference amount. In addition, inspired by the idea of using two features, we propose a time series classification method based on few feature pairs using dynamic time warping and nearest neighbor

An Empirical Investigation of Filter Attribute Selection Techniques for Software Quality Classification

Attribute selection is an important activity in data preprocessing for software quality modeling and other data mining problems. The software quality models have been used to improve the fault detection process. Finding faulty components in a software system during early stages of software development process can lead to a more reliable final product and can reduce development and maintenance costs. It has been shown in some studies that prediction accuracy of the models improves when irrelevant and redundant features are removed from the original data set. In this study, we investigated four filter attribute selection techniques, Automatic Hybrid Search (AHS), Rough Sets (RS), Kolmogorov-Smirnov (KS) and Probabilistic Search (PS) and conducted the experiments by using them on a very large telecommunications software system. In order to evaluate their classification performance on the smaller subsets of attributes selected using different approaches, we built several classification models using five different classifiers. The empirical results demonstrated that by applying an attribution selection approach we can build classification models with an accuracy comparable to that built with a complete set of attributes. Moreover, the smaller subset of attributes has less than 15 percent of the complete set of attributes. Therefore, the metrics collection, model calibration, model validation, and model evaluation times of future software development efforts of similar systems can be significantly reduced. In addition, we demonstrated that our recently proposed attribute selection technique, KS, outperformed the other three attribute selection techniques

An Evaluation of the Wisconsin Breast Cancer Dataset using Ensemble Classifiers and RFE Feature Selection Technique

Breast cancer represents one of the deadliest diseases that records a high number of death rate annually. It is the most common type of cancer and the main cause of death among women worldwide. Machine learning (ML) approach is an effective way to classify data, especially in medical field. It is widely used for classification and analysis to make decisions. In this paper, a performance comparison between two ensemble ML classifiers: Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) on the Wisconsin Breast Cancer Dataset (WBCD) is conducted. The main objective of this study is to assess the correctness of the classifiers with respect to their efficiency and effectiveness in classifying the dataset. This was done by utilizing all and reduced features of the dataset that were generated with Recursive Feature Elimination (RFE) feature selection technique. Four metrics were used in the study: Accuracy, Precision, Recall and F1-Score to evaluate the classifiers. All experiments were executed within Anaconda Environment with Jupyter Notebook and conducted using Python programming language. Experimental result shows that XGBoost with 5 reduced feature using RFE feature selection technique gives the highest accuracy (99.02%) with lowest error rate