Low birthweight is a widely used indicator of newborn health. This study investigates the association of birthweight <2.5 kg (LBW) with a wide range of factors related to geo-demographics, maternal health and pregnancy history in public hospitals at Peshawar, North West Frontier Province (NWFP) Pakistan. It is noted that that Low birthweight may arise for two different reasons, one related to gestational age and the other corresponding to births that are small for gestational age (SGA).

Time-to-event analysis is important in a wide range of applications from clinical prognosis to risk modeling for credit scoring and insurance. In risk modeling, it is sometimes required to make a simultaneous assessment of the hazard arising from two or more mutually exclusive factors. This paper applies to an existing neural network model for competing risks (PLANNCR), a Bayesian regularization with the standard approximation of the evidence to implement automatic relevance determination (PLANNCR-ARD). The theoretical framework for the model is described and its application is illustrated with reference to local and distal recurrence of breast cancer, using the data set of Veronesi (1995).

Genome-Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. The main objective is to find single nucleotide polymorphisms (SNPs) that influence a particular phenotype. GWAS use a p-value threshold of $5\star 10^{-8}$ to identify highly ranked SNPs. While this approach has proven useful for detecting disease-susceptible SNPs, evidence has shown that many of these are, in fact, false positives. Consequently, there is some ambiguity about the most suitable threshold for claiming genome-wide significance. Many believe that using lower p-values will allow us to investigate the joint epistatic interactions between SNPs and provide better insights into phenotype expression. In this paper, we propose a novel framework, based on nonlinear transformations of combinatorically large SNP data, using stacked autoencoders, to identify higher-order SNP interactions. We focus on the challenging problem of classifying preterm births. Latent representations from original SNP sequences are used to initialize a deep learning classifier before it is fine-tuned for classification tasks. The findings show that important information pertaining to epistasis can be extracted from 4666 raw SNPs generated using logistic regression (p-value= $5\star 10^{-8}$ ) and used to fit a deep learning model and obtain results (Sen=0.9289, Spec=0.9591, Gini=0.9651, Logloss=0.3080, AUC=0.9825, MSE=0.0942) using 500 hidden nodes.

Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the wellbeing of the foetus during antenatal care. This approach has raised concerns among professionals with regards to inter- and intra-variability where clinical diagnosis only has a 30% positive predictive value when classifying pathological outcomes. Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability. This is only possible however when class distributions are equal which is rarely the case in clinical trials where case-control observations are heavily skewed in favour of normal outcomes. Classes can be balanced using either synthetic data derived from resampled case training data or by decreasing the number of control instances. However, this either introduces bias or removes valuable information. Concerns have also been raised regarding machine learning studies and their reliance on manually handcrafted features. While this has led to some interesting results, deriving an optimal set of features is considered to be an art as well as a science and is often an empirical and time consuming process. In this article, we address both of these issues and propose a novel CTG analysis methodology that a) splits CTG time-series signals into n-size windows with equal class distributions, and b) automatically extracts features from time-series windows using a one dimensional convolutional neural network (1DCNN) and multilayer perceptron (MLP) ensemble. Collectively, the proposed approach normally distributes classes and removes the need to handcrafted features from CTG traces. The 1DCNN-MLP models trained with several windowing strategies are evaluated to determine how well they can distinguish between normal and pathological birth outcomes. Our proposed method achieved good results using a window size of 200 with 80% (95% CI: 75%, 85%) for Sensitivity, 79% (95% CI: 73%, 84%) for Specificity and 86% (95% CI: 81%, 91%) for the Area Under the Curve. The 1DCNN approach is also compared with several traditional machine learning models, which all failed to improve on the windowing 1DCNN strategy proposed.

Running impact forces expose the body to biomechanical loads leading to beneficial adaptations, but also risk of injury. High-intensity running tasks, especially, are deemed highly demanding for the musculoskeletal system, but loads experienced during these actions are not well understood. To eventually predict GRF and understand the biomechanical loads experienced during such activities in greater detail, this study aimed to (1) examine the feasibility of using a simple two mass-spring-damper model, based on eight model parameters, to reproduce ground reaction forces (GRFs) for high-intensity running tasks and (2) verify whether the required model parameters were physically meaningful. This model was used to reproduce GRFs for rapid accelerations and decelerations, constant speed running and maximal sprints. GRF profiles and impulses could be reproduced with low to very low errors across tasks, but subtler loading characteristics (impact peaks, loading rate) were modelled less accurately. Moreover, required model parameters varied strongly between trials and had minimal physical meaning. These results show that although a two mass-spring-damper model can be used to reproduce overall GRFs for high-intensity running tasks, the application of this simple model for predicting GRFs in the field and/or understanding the biomechanical demands of training in greater detail is likely limited.

K-means clustering is widely used for exploratory data analysis. While its dependence on initialisation is well-known, it is common practice to assume that the partition with lowest sum-of-squares (SSQ) total i.e. within cluster variance, is both reproducible under repeated initialisations and also the closest that k-means can provide to true structure, when applied to synthetic data. We show that this is generally the case for small numbers of clusters, but for values of k that are still of theoretical and practical interest, similar values of SSQ can correspond to markedly different cluster partitions. This paper extends stability measures previously presented in the context of finding optimal values of cluster number, into a component of a 2-d map of the local minima found by the k-means algorithm, from which not only can values of k be identified for further analysis but, more importantly, it is made clear whether the best SSQ is a suitable solution or whether obtaining a consistently good partition requires further application of the stability index. The proposed method is illustrated by application to five synthetic datasets replicating a real world breast cancer dataset with varying data density, and a large bioinformatics dataset.