Determining Efficient Machine Learning Techniques for Grading of Knee Osteoarthritis

Osteoarthritis (OA) of the Knee is a degenerative joint disease mainly caused due to loss of articular cartilages. The paper introduces an approach to quantify knee osteoarthritis (OA) severity using KL grades. This approach combines EDA (Exploratory Data Analysis), Pre-processing and Feature Engineering techniques. The amount of damage to the knee can be graded using KL scale (0-4). The automated detection of Knee Osteoarthritis (KOA) based on KL grades which corresponds to severity stages has been given in the paper. In the study public dataset from Osteoarthritis Initiative (OAI) has been used to evaluate the proposed approach with very promising results. Different accuracy metrices like F1 score, Receiver operating characteristic curve (ROC), Area Under Curve (AUC) and Precision were used to find the best algorithm amongst the classification models in Machine learning. Random forest and Decision trees algorithms were considered efficient giving an accuracy of 96.9% and 91.6% respectively. Our study is an economically better approach when compared to x-rays for OA detectio

Advanced analyses of physiological signals and their role in Neonatal Intensive Care

Preterm infants admitted to the neonatal intensive care unit (NICU) face an array of life-threatening diseases requiring procedures such as resuscitation and invasive monitoring, and other risks related to exposure to the hospital environment, all of which may have lifelong implications. This thesis examined a range of applications for advanced signal analyses in the NICU, from identifying of physiological patterns associated with neonatal outcomes, to evaluating the impact of certain treatments on physiological variability. Firstly, the thesis examined the potential to identify infants at risk of developing intraventricular haemorrhage, often interrelated with factors leading to preterm birth, mechanical ventilation, hypoxia and prolonged apnoeas. This thesis then characterised the cardiovascular impact of caffeine therapy which is often administered to prevent and treat apnoea of prematurity, finding greater pulse pressure variability and enhanced responsiveness of the autonomic nervous system. Cerebral autoregulation maintains cerebral blood flow despite fluctuations in arterial blood pressure and is an important consideration for preterm infants who are especially vulnerable to brain injury. Using various time and frequency domain correlation techniques, the thesis found acute changes in cerebral autoregulation of preterm infants following caffeine therapy. Nutrition in early life may also affect neurodevelopment and morbidity in later life. This thesis developed models for identifying malnutrition risk using anthropometry and near-infrared interactance features. This thesis has presented a range of ways in which advanced analyses including time series analysis, feature selection and model development can be applied to neonatal intensive care. There is a clear role for such analyses in early detection of clinical outcomes, characterising the effects of relevant treatments or pathologies and identifying infants at risk of later morbidity