A methodology for automatic classification of breast cancer immunohistochemical data using semi-supervised fuzzy c-means

Previously, a semi-manual method was used to identify six novel and clinically useful classes in the Nottingham Tenovus Breast Cancer dataset. 663 out of 1,076 patients were classified. The objectives of our work is three folds. Firstly, our primary objective is to use one single automatic method (post-initialisation) to reproduce the six classes for the 663 patients and to classify the remaining 413 patients. Secondly, we explore using semi-supervised fuzzy c-means with various distance metrics and initialisation techniques to achieve this. Thirdly, the clinical characteristics of the 413 patients are examined by comparing with the 663 patients. Our experiments use various amount of labelled data and 10-fold cross validation to reproduce and evaluate the classification. ssFCM with Euclidean distance and initialisation technique by Katsavounidis et al. produced the best results. It is then used to classify the 413 patients. Visual evaluation of the 413 patients’ classifications revealed common characteristics as those previously reported. Examination of clinical characteristics indicates significant associations between classification and clinical parameters. More importantly, association between classification and survival based on the survival curves is shown

On the relevance of preprocessing in predictive maintenance for dynamic systems

The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way. We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems

A Training Set Selection Strategy for a Universal Near-Infrared Quantitative Model

The purpose of this article is to propose an empirical solution to the problem of how many clusters of complex samples should be selected to construct the training set for a universal near infrared quantitative model based on the Næs method. The sample spectra were hierarchically classified into clusters by Ward’s algorithm and Euclidean distance. If the sample spectra were classified into two clusters, the 1/50 of the largest Heterogeneity value in the cluster with larger variation was set as the threshold to determine the total number of clusters. One sample was then randomly selected from each cluster to construct the training set, and the number of samples in training set equaled the number of clusters. In this study, 98 batches of rifampicin capsules with API contents ranging from 50.1% to 99.4% were studied with this strategy. The root mean square errors of cross validation and prediction were 2.54% and 2.31% for the model for rifampicin capsules, respectively. Then, we evaluated this model in terms of outlier diagnostics, accuracy, precision, and robustness. We also used the strategy of training set sample selection to revalidate the models for cefradine capsules, roxithromycin tablets, and erythromycin ethylsuccinate tablets, and the results were satisfactory. In conclusion, all results showed that this training set sample selection strategy assisted in the quick and accurate construction of quantitative models using near-infrared spectroscopy