scholarly journals Early Diagnosis of Coronary Artery Disease using UCI Data set

The focus of this project is based on both processing potential clinical features and implementing the classification architecture for detection of cardiac abnormality. The milestone of this first year involves analysis and investigation of different feature selection and transformation methods and theoretical modeling of single and hybrid systems by optimizing associated systematic parameters for better precision and recall. The importance of this paper is due to its clear objectives where an optimized and advanced system is designed and implemented for the cardiac disease utilizing computer aided diagnosis techniques for data and signal processing. The methodology is clear and trait forward using the hybrid approach of data mining techniques integrated to deliver enhanced performance on desired data set. In this paper comparative classification approaches are integrated to enhance system detection rate and decrease false alarms. The study focuses on feature preprocessing to select suitable feature subsets for classification algorithms like clustering (unsupervised learning) and SVM (supervised learning) which helps in generalizing the diagnosis system to detect unseen abnormality. For this study, will first apply statistical measures such as scoring ranking for clinical datasets consisting the electrocardiogram (ECG) features to reduce its dimension by eliminating irrelevant features. In the second part, will apply parametric tuned classification algorithms for selected feature subsets. The third part is to quantify the severity of CAD. At the last performance of the proposed system will be compared with other applied classification techniques in terms of accuracy, sensitivity and specificity.

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 62
Author(s):  
Jakub Svatos ◽  
Jan Holub ◽  
Jan Belak

<p class="Abstract">Currently, acoustic detection techniques of gunshots (gunshot detection and its classification) are increasingly being used not only for military applications but also for civilian purposes. Detection, localisation, and classification of a dangerous event such as gunshots employing acoustic detection is a perspective alternative to visual detection, which is commonly used. In some situations, to detect and localise the source of a gunshot, an automatic acoustic detection system, which can classify the caliber, may be preferable. This paper presents a system for acoustic detection, which can detect, localise and classify acoustic events such as gunshots. The system has been tested in open and closed shooting ranges and tested firearms are 9 mm short gun, 6.35 mm short gun, .22 short gun, and .22 rifle gun with various subsonic and supersonic ammunition. As ‘false alarms’, sets of different impulse acoustic events like door slams, breaking glass, etc. have been used. Localisation and classification algorithms are also introduced. To successfully classify the tested acoustic signals, Continuous Wavelet and Mel Frequency Transformation methods have been used for the signal processing, and the fully two-layer connected neural network has been implemented. The results show that the acoustic detector can be used for reliable gunshot detection, localisation, and classification.</p>


Ring ◽  
2004 ◽  
Vol 26 (2) ◽  
pp. 89-92
Author(s):  
John Morgan

Wing lengths of Clamorous Reed Warblers Acrocephalus stentoreus in Israel Wing length measurements taken from first-year, pre- and post-moulting (annual, complete) Clamorous Reed Warblers were recorded at a site in northern Israel. The resulting data set was examined using a time-series of residuals (CUSUM). Results from this analysis can explain the reported heterogeneity found in a comparable data set by Merom et al. (1999). Further observations made in their paper are rebutted: (1) an implied assumption that Reed Warbler (Acrocephalus scirpaceus) spring migration in Israel ends by 1 May is contrary to other publications; (2) the late autumn occurence in N Israel of longer-winged 1st cal. yr. Reed Warblers, unconvincingly explained as either delayed migration by larger individuals or post fledging feather growth, is most likely due to birds from different provenances origins moving at different seasons; (3) growth during adulthood in Reed Warbler is not a new discovery, though presented as such.


Author(s):  
Varun Sapra ◽  
M.L Saini ◽  
Luxmi Verma

Background: Cardiovascular diseases are increasing at an alarming rate with very high rate of mortality. Coronary artery disease is one of the type of cardiovascular disease, which is not easily diagnosed in its early stage. Prevention of Coronary Artery Disease is possible only if it is diagnosed, at early stage and proper medication is done. Objective: An effective diagnosis model is important not only for the early diagnosis but also to check the severity of the disease. Method: In this paper, a hybrid approach is followed, with the integration of deep learning (multi-layer perceptron) with Case based reasoning to design analytical framework. This paper suggests two phases of the study, one in which the patient is diagnosed for Coronary artery disease and in second phase, if the patient is suffering from the disease then employing Case based reasoning to diagnose the severity of the disease. In the first phase, multilayer perceptron is implemented on reduced dataset and with time-based learning for stochastic gradient descent respectively. Results: The classification accuracy is increase by 4.18 % with reduced data set using deep neural network with time based learning. In second phase, if the patient is diagnosed as positive for Coronary artery disease, then it triggers the Case based reasoning system to retrieve from the case base, the most similar case to predict the severity for that patient. The CBR model achieved 97.3% accuracy. Conclusion: The model can be very useful for medical practitioners as a supporting decision system and thus can save the patients from unnecessary medical expenses on costly tests and can improve the quality and effectiveness of medical treatment.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenny Alderden ◽  
Kathryn P. Drake ◽  
Andrew Wilson ◽  
Jonathan Dimas ◽  
Mollie R. Cummins ◽  
...  

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.


2021 ◽  
Author(s):  
Andrea C. Hupman

Classification algorithms predict the class membership of an unknown record. Methods such as logistic regression or the naïve Bayes algorithm produce a score related to the likelihood that a record belongs to a particular class. A cutoff threshold is then defined to delineate the prediction of one class over another. This paper derives analytic results for the selection of an optimal cutoff threshold for a classification algorithm that is used to inform a two-action decision in the cases of risk aversion and risk neutrality. The results provide insight to how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for both the risk neutral and risk averse decision makers. The optimal risk averse threshold is not reliably above or below the optimal risk neutral threshold, but the relation depends on the parameters of a particular application. The results further show the risk averse optimal threshold is insensitive to the size of the data set or the magnitude of the costs, but instead is sensitive to the proportion of positive records in the data and the ratio of costs. Numeric examples and sensitivity analysis derive further insight. Results show the percent value gap from a misspecified risk attitude increases as the specificity of the classification algorithm decreases.


Purpose. Assess the current state of the Southern Bug River basin and the degree of anthropogenic impact on it (transformation) Methods. The methodology of complex field geographic research (according to S. Kurlov) was chosen for the research. On its basis, water samples were taken at the appropriate points of the river basin, which directly covered the Southern Bug River itself, as well as its tributaries, and the composition of coastal soils was analyzed. Laboratory experiments were performed on the basis of a licensed laboratory of the Uman National University of Horticulture Results. Based on the analysis of the data set of the main hydrochemical indicators of the surface water quality of the Southern Bug river basin and the analysis of the soil, it was found at some experimental points that the MPC content of the studied indicators was exceeded. The content is exceeded at all sampling points, due to the high content of complexes with humic acid salts, secondary waterlogging of reclaimed lands. The nitrite content is exceeded in the following places: r. Southern Bug (Vinnitsa and Ladyzhin), r. Ustya (Nemyriv), r. Udych litskiy district) and r. Dohna (Bershad district). The main reason for the intake of nitrates into the water is the washout of fertilizers from fields and vegetable gardens. All surface water bodies have increased water hardness, its acidity is within normal limits. The soils adjacent to the streams under consideration bear clear signs of hydrogenic accumulation of substances (carbonates, gypsum, salts) and deltaic conditions of formation (horizontal stratification, horizons buried, remnants of freshwater fauna). Conclusions. The concentration of pollutants increases with the approach of the tributaries of the Southern Bug River and the relief formed by them to their base of erosion. Excess concentrations of alkaline hydrolyzed nitrogen, as well as phosphorus and potassium compounds at the sampling points indicate a high level of intensity of the farming system, in which the use of agrochemicals is given priority. However, long-term ingress of these compounds into the water body will accelerate the process of eutrophication in the near future. The loss of humus in the soils of agricultural lands adjacent to the rivers Sob, Dokhna, Ustya was revealed. In general, the geosystem of the Southern Bug River suffers from a complex anthropogenic impact, the main source of which is agricultural production.


2021 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
David D. Perrodin

<p>Recent events in Thailand in reference to the teaching of phonics for better comprehension of English vocabulary have highlighted the overuse of identifying letter-sound relationships in English by utilizing the familiar Thai orthography to assist developing Thai EFL learners. This paper investigated the long-term effects of using such pedagogy on recognizing regular and irregular Grapheme-Phoneme Correspondences (GPC) in English by Thai undergraduate students. To address this matter, the study used a convenience sampling of 373 first-year university students from 11 general education English classes at a mid-sized private university near Bangkok, Thailand. The familiar English poem <em>I Take It You Already Know</em> was employed for data collection, for it consists of a practical ratio of frequent and infrequent English grapheme-phoneme correspondences. Extensive lists of recognized grapheme-phoneme correspondences were used to identify the frequent or regular, and the infrequent or irregular main phoneme present in each of the 60 most frequently queried content words of the poem. Point-Biserial Correlation was employed to measure the strength of association between the frequency occurrence of the most queried content words from the data set, and the examined weighted word frequency data. The findings suggest that, in general, the Thai undergraduate students demonstrated an overall lack of recognition of regular and irregular Grapheme-Phoneme Correspondences of English. </p>


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 35-36
Author(s):  
Sandra Tong ◽  
Robert P. Numerof ◽  
Jane Datangel ◽  
Esteban Masuda

Introduction: Fostamatinib is an oral, potent inhibitor of spleen tyrosine kinase (SYK) with proven efficacy and a manageable safety profile for the treatment of ITP. SYK is situated in an intracellular signaling pathway upstream of Bruton's tyrosine kinase (BTK). Long-term safety data on fostamatinib at various dosing regimens (up to 150 mg BID) has been collected in &gt;4000 patients with ITP, rheumatoid arthritis (RA), and other autoimmune, allergic and neoplastic disorders. The safety and tolerability of fostamatinib were consistent across different patient populations (apart from disease specific events). We present a summary analysis of the fostamatinib safety data from the ITP and RA studies. Methods: Fostamatinib safety data from 2 randomized, double-blind, placebo-controlled, phase 3 studies and the long-term, open-label, extension (OLE) study in ITP were pooled and are based on a starting dose of 200 mg/day, which was increased to 300 mg/day after 4 weeks in 88% of patients. Fostamatinib safety data from 13 phase 2/3 studies in RA were pooled and are based on a dosing regimen of 100-150 mg/day (n=1232) or 200-300 mg/day (n=2205). Results: The pooled data set for ITP included 146 patients; 60% were female, and the median age was 53 years (range 20-88). The mean duration of fostamatinib treatment was 19 months (range &lt;1-62 months), representing 229 patient exposure years. Adverse events (AEs) were reported in 87% of patients, and 63% were mild to moderate. Serious AEs were reported in 31% of patients. The incidence of diarrhea, hypertension, alanine aminotransferase increase (ALT), and aspartate aminotransferase (AST) increase was evaluated in 58 patients who received fostamatinib for ≥1 year. This enabled a comparison of the incidence of these AEs in quartiles over the first year to assess the cumulative effects of fostamatinib. The AEs were reported with decreasing frequency during the second, third, and fourth quarters of fostamatinib treatment compared with the first quarter of the initial year of treatment in the 58 patients (see Figure 1). In the same 58 patients, the use of rescue therapy decreased while median platelet counts increased each quarter of the first year. The pooled data set for RA included 3437 patients who received fostamatinib; 83% were female, and the median age was 54 (range 18 -87). The mean duration of treatment was 18 months (range &lt;1-81) representing 5134 patient exposure years. AEs were reported in 86% of RA patients and were mild to moderate in 73% of RA patients. Serious AEs occurred in 14%. In the placebo-controlled RA studies, 2414 patients received fostamatinib with 823 patient exposure years and 1169 received placebo with 367 patient exposure years. Despite a two-fold (125%) increase in exposure with fostamatinib vs placebo (823 vs 367 patient exposure years), there was only a 26% increase in AEs with fostamatinib vs placebo (68% vs 54%). The most common events in the ITP and RA studies were diarrhea (36% and 24%), hypertension (22% and 19%) and nausea (19% and 8%), apart from disease-related AEs. Epistaxis (19% and 0.5%), petechiae (15% and 0.3%), contusion (12% and 2%), and fatigue (10% and 2%) are associated with ITP and were uncommon in the RA population. Rheumatoid arthritis was reported as an AE in 9% of patients with RA and in none with ITP. Some AEs may be dose-related, and one-third of the RA patients were on lower dosages (100-150 mg/day) than were generally given in the ITP trials (200-300 mg/day). Conclusions: Fostamatinib has been evaluated in &gt;4000 patients across different disease populations. Fostamatinib has a consistent and manageable safety profile. No new safety signals and no cumulative toxicity were observed with up to 81 months (6.8 years) of continuous treatment. Figure 1 Disclosures Tong: Rigel: Current Employment, Current equity holder in publicly-traded company. Numerof:Rigel: Current Employment, Current equity holder in publicly-traded company. Datangel:Rigel: Current Employment, Current equity holder in publicly-traded company. Masuda:Rigel: Current Employment, Current equity holder in publicly-traded company.


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