Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Terminal defect analysis and classification methodology for automotive industry

The impact of end customer quality complaints with direct relationship with automotive components has presented negative trend at European level for the entire automotive industry. Thus, this research proposal is to concentrate efforts on the most important items of Pareto chart and understand the failure type and the mechanism involved, link and impact of the project and parameters on the process, ending it with the development of one of the company’s most desired tool, that hosted this project – European methodology of terminals defects classification, and listing real opportunities for improvement based on measurement and analysis of actual data. Through the development of terminals defects classification methodology, which is considered a valuable asset to the company, all the other companies of the YAZAKI’s group will be able to characterize terminals as brittle or ductile, in order to put in motion, more efficiently, all the other different existing internal procedures for the safeguarding of the components, improving manufacturing efficiency. Based on a brief observation, nothing can be said in absolute sense, concerning the failure causes. Base materials, project, handling during manufacture and storage, as well as the cold work performed by plastic deformation, all play an important role. However, it was expected that this failure has been due to a combination of factors, in detriment of the existence of a single cause. In order to acquire greater knowledge about this problem, unexplored by the company up to the date of commencement of this study, was conducted a thorough review of existing literature on the subject, real production sites were visited and, of course, the actual parts were tested in lab environment. To answer to many of the major issues raised throughout the investigation, were used extensively some theoretical concepts focused on the literature review, with a view to realizing the relationship existing between the different parameters concerned. Should here be stated that finding technical studies on copper and its alloys is really hard, not being given all the desirable information. This investigation has been performed as a YAZAKI Europe Limited Company project and as a Master Thesis for Instituto Superior de Engenharia do Porto, conducted during 9 months between 2012/2013.