Automatic Induction of Classification Rules from Examples Using N-Prism

www.dis.port.ac.uk/~bramerma One of the key technologies of data mining is the automatic induction of rules from examples, particularly the induction of classification rules. Most work in this field has concentrated on the generation of such rules in the intermediate form of decision trees. An alternative approach is to generate modular classification rules directly from the examples. This paper seeks to establish a revised form of the rule generation algorithm Prism as a credible candidate for use in the automatic induction of classification rules from examples in practical domains where noise may be present and where predicting the classification for previously unseen instances is the primary focus of attention

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Extensive Copy-Number Variation of Young Genes across Stickleback Populations

MM received funding from the Max Planck innovation funds for this project. PGDF was supported by a Marie Curie European Reintegration Grant (proposal nr 270891). CE was supported by German Science Foundation grants (DFG, EI 841/4-1 and EI 841/6-1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The Formation and Evolution of the First Massive Black Holes

The first massive astrophysical black holes likely formed at high redshifts (z>10) at the centers of low mass (~10^6 Msun) dark matter concentrations. These black holes grow by mergers and gas accretion, evolve into the population of bright quasars observed at lower redshifts, and eventually leave the supermassive black hole remnants that are ubiquitous at the centers of galaxies in the nearby universe. The astrophysical processes responsible for the formation of the earliest seed black holes are poorly understood. The purpose of this review is threefold: (1) to describe theoretical expectations for the formation and growth of the earliest black holes within the general paradigm of hierarchical cold dark matter cosmologies, (2) to summarize several relevant recent observations that have implications for the formation of the earliest black holes, and (3) to look into the future and assess the power of forthcoming observations to probe the physics of the first active galactic nuclei.Comment: 39 pages, review for "Supermassive Black Holes in the Distant Universe", Ed. A. J. Barger, Kluwer Academic Publisher

Why Are There Social Gradients in Preventative Health Behavior? A Perspective from Behavioral Ecology

Background: Within affluent populations, there are marked socioeconomic gradients in health behavior, with people of lower socioeconomic position smoking more, exercising less, having poorer diets, complying less well with therapy, using medical services less, ignoring health and safety advice more, and being less health-conscious overall, than their more affluent peers. Whilst the proximate mechanisms underlying these behavioral differences have been investigated, the ultimate causes have not. Methodology/Principal Findings: This paper presents a theoretical model of why socioeconomic gradients in health behavior might be found. I conjecture that lower socioeconomic position is associated with greater exposure to extrinsic mortality risks (that is, risks that cannot be mitigated through behavior), and that health behavior competes for people’s time and energy against other activities which contribute to their fitness. Under these two assumptions, the model shows that the optimal amount of health behavior to perform is indeed less for people of lower socioeconomic position. Conclusions/Significance: The model predicts an exacerbatory dynamic of poverty, whereby the greater exposure of poor people to unavoidable harms engenders a disinvestment in health behavior, resulting in a final inequality in health outcomes which is greater than the initial inequality in material conditions. I discuss the assumptions of the model, and it

Asymmetric recurrent laryngeal nerve conduction velocities and dorsal cricoarytenoid muscle electromyographic characteristics in clinically normal horses

The dorsal cricoarytenoid (DCA) muscles, are a fundamental component of the athletic horse’s respiratory system: as the sole abductors of the airways, they maintain the size of the rima glottis which is essential for enabling maximal air intake during intense exercise. Dysfunction of the DCA muscle leads to arytenoid collapse during exercise, resulting in poor performance. An electrodiagnostic study including electromyography of the dorsal cricoarytenoid muscles and conduction velocity testing of the innervating recurrent laryngeal nerves (RLn) was conducted in horses with normal laryngeal function. We detected reduced nerve conduction velocity of the left RLn, compared to the right, and pathologic spontaneous activity (PSA) of myoelectrical activity within the left DCA muscle in half of this horse population and the horses with the slowest nerve conduction velocities. The findings in this group of horses are consistent with left sided demyelination and axonal loss, consistent with Recurrent Laryngeal Neuropathy (RLN), a highly prevalent degenerative disorder of the RLn in horses that predominantly affects the left side. The detection of electromyographic changes compatible with RLN in clinically unaffected horses is consistent with previous studies that identified “subclinical” subjects, presenting normal laryngeal function despite neuropathologic changes within nerve and muscle confirmed histologically

Cardiac damage after treatment of childhood cancer: A long-term follow-up

<p>Abstract</p> <p>Background</p> <p>With improved childhood cancer cure rate, long term sequelae are becoming an important factor of quality of life. Signs of cardiovascular disease are frequently found in long term survivors of cancer. Cardiac damage may be related to irradiation and chemotherapy.</p> <p>We have evaluated simultaneous influence of a series of independent variables on the late cardiac damage in childhood cancer survivors in Slovenia and identified groups at the highest risk.</p> <p>Methods</p> <p>211 long-term survivors of different childhood cancers, at least five years after treatment were included in the study. The evaluation included history, physical examination, electrocardiograpy, exercise testing and echocardiograpy. For analysis of risk factors, beside univariate analysis, multivariate classification tree analysis statistical method was used.</p> <p>Results and Conclusion</p> <p>Patients treated latest, from 1989–98 are at highest risk for any injury to the heart (73%). Among those treated earlier are at the highest risk those with Hodgkin's disease treated with irradiation above 30 Gy and those treated for sarcoma. Among specific forms of injury, patients treated with radiation to the heart area are at highest risk of injury to the valves. Patients treated with large doses of anthracyclines or concomitantly with anthracyclines and alkylating agents are at highest risk of systolic function defect and enlarged heart chambers. Those treated with anthracyclines are at highest risk of diastolic function defect. The time period of the patient's treatment is emerged as an important risk factor for injury of the heart.</p

Cement-in-cement stem revision for Vancouver type B periprosthetic femoral fractures after total hip arthroplasty: A 3-year follow-up of 23 cases

Background and purpose Revision surgery for periprosthetic femoral fractures around an unstable cemented femoral stem traditionally requires removal of existing cement. We propose a new technique whereby a well-fixed cement mantle can be retained in cases with simple fractures that can be reduced anatomically when a cemented revision is planned. This technique is well established in femoral stem revision, but not in association with a fracture

Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

<p>Abstract</p> <p>Background</p> <p>By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.</p> <p>Results</p> <p>First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.</p> <p>Conclusion</p> <p>By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.</p