Detrano,R., Janosi,A., Steinbrunn,W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disese. American Hournal of Cardiology, 64, 304-310. Home/Search Document Not in Database Summary Related Articles Check |
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Distributed Data Mining Systems - Prodromidis (1999) (Correct)
....been developed and applied to many problems in diverse areas. Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value [ Mitchell, 1997b ] Machine learning algorithms have been deployed in heart disease diagnosis [ Detrano et al. 1989 ] in predicting glucose levels for diabetic patients [ Carson Fischer, 1990 ] in detecting credit card fraud [ Stolfo et al. 1997a ] in steering vehicles driving autonomously on public highways at 70 miles an hour [ Pomerleau, 1992 ] in predicting stock option pricing [ Malliaris ....
Detrano, R.; Janosi, A.; Steinbrunn, W.; Pfisterer, M.; Schmid, J.; Sandhu, S.; Guppy, K.; Lee, S.; and Froelicher, V. 1989. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology 64:304--310.
Converting A Trained Neural Network To A Decision Tree - Dectext -.. - Boz (2000) (Correct)
....the datasets Zoo, Vote, Vote 3, Heart, and Housing. Zoo dataset is an animal classi cation database. Animals are divided into 7 sets according to their features. Vote dataset contains votes from U.S. House of Representatives during the 98th congress [Schlimmer and Fisher, 1986] Heart dataset [Detrano et al. 1987] is concerned about heart disease diagnosis. The data was collected from the Cleveland Clinic Foundation. In this dataset we changed the feature number of major vessels from continuous to discrete with values 0,1,2,3 . Housing dataset concerns housing values in suburbs of Boston. The original ....
Detrano, R., Janosi, A., Steinbrunn, W., P setrer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., and Froelicher, V. (1987). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304-410.
Extracting Decision Trees From Trained Neural Networks - Boz (2002) (Correct)
....we created unpruned trees by using C4.5 (we used the command c4.5 m1 c100 f file base u) 4.1 Datasets Used For Evaluating DecText We used the datasets Vote, Vote 3, Heart, and Housing. Vote dataset contains votes from U.S. House of Representatives during the 98th congress [14] Heart dataset [9] is concerned about heart disease diagnosis. The data was collected from the Cleveland Clinic Foundation. In this dataset we changed the feature number of major vessels from continuous to discrete with values 0,1,2,3 . Housing dataset concerns housing values in suburbs of Boston. The original ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisetrer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--410, 1987.
A Study on Rule Extraction from Neural Networks Applied to.. - Bologna (Correct)
....chosen 9 real world databases. Eight of them have been retrieved from the repository for Machine Learning at the University of California Irvine 1 . We denote these diagnosis problems to as: 1. Wisconsin Breast Cancer 1 (BC1) 8] 2. Wisconsin Breast Cancer 2 (BC2) 9] 3. Heart Disease 1 (HD1) [6]; 4. Heart Disease 2 (HD2) 1] 5. Dermatology (DER) 5] 6. Liver Disorders (LVD) 7. Pima Indians Diabetes (PID) 13] 8. Thyroid Gland 1 (TG1) 4] 9. Thyroid Gland 2 (TG2) 12] 3.1 Description As a brief description, databases BC1 and BC2 are related to benign and malignant breast cancer ....
Detrano, R., Janosi, A., Steinbrunn, W., PfistererM., Schmid, J., Sandhu S., Guppy, K., Lee S., Froelicher V.: International application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease. In American Journal of Cardiology (1989), 64 304--310.
Optimisation on Support Vector Machines - Jo Ao Pedro (Correct)
....are available in [3] The breast cancer databases (below referred to as breast ) was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg [1] Another test is the heart disease diagnosis test ( heart ) which was collected in the Cleveland Clinic Foundation [4]. The sonar test corresponds to the data set used by Gorman and Sejnowski in their study of the classi cation of sonar signals using a neural network[5] The task is to train a network to discriminate between sonar signals bounced o a metal cylinder and those bounced o a roughly cylindrical ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Psterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304-310, 1989.
Meta-Learning in Distributed Data Mining Systems: Issues.. - Prodromidis, Chan, al. (2000) (34 citations) (Correct)
....have already been developed and applied to many problems in diverse areas. Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value [45] Machine learning algorithms 1 have been deployed in heart disease diagnosis [61], in predicting glucose levels for diabetic patients [22] in detecting credit card fraud [65] in steering vehicles driving autonomously on public highways at 70 miles an hour [50] in predicting stock option pricing [46] and in computing customized electronic newspapers[33] to name a few ....
R.Detrano, A.Janosi, W.Steinbrunn, M.Pfisterer, J.Schmid, S.Sandhu, K.Guppy, S.Lee, and V.Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Support Vector Machines for Linear Programming: Motivation.. - Pedroso, Murata (1999) (Correct)
....are available in [5] The breast cancer databases (below referred to as BREAST ) was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg [2] Another test is the heart disease diagnosis test ( HEART ) which was collected in the Cleveland Clinic Foundation [6]. The SONAR test cor10 responds to the data set used by Gorman and Sejnowski in their study of the classi cation of sonar signals using a neural network[7] The task is to train a network to discriminate between sonar signals bounced o a metal cylinder and those bounced o a roughly ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Psterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304-310, 1989.
Experimental Analysis of Aspects of the.. - Squires, Jr., Shavlik (1991) (5 citations) (Correct)
....an easy problem, we chose PARITY6. Given six inputs, the desired output is 0.5 if the number of 0.5 inputs is odd, and is 0.5 otherwise. We randomly divided the data into 32 training and 32 test patterns. For medium complexity, we selected the HEART DISEASE diagnosis problem presented in [2]. This noisy, real world problem maps 25 inputs onto two outputs. The input units encode eight nominally valued (i.e. possible values from an unordered, finite set) and six numericallyvalued features. The output units separately represent the two possible categories: healthy and diseased. We used ....
Detrano, R. (unpublished manuscript). International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease. V. A. Medical Center, Long Beach, CA.
Utilizing the Topology Preserving Property of Self-Organizing .. - van der Putten (1996) (Correct)
....algorithm. The following set of benchmarks was used: ffl Datasets from the Proben1 benchmark collection [Prechelt, 1994] heartc Cleveland Heart database: decide if one of four main vessels is reduced in diameter by approximately 50 . Features describe sex, smoking and drinking habits etc. [Detrano et al. 1989] diabetes Pima Indians Diabetes database: diagnose diabetes of Pima Indian based on personal information and medical examinations. Smith et al. 1988] cancer Wisconsin breast cancer database: classify tumor as benign or malignant based on microscopic examination of cells. Mangasarian and ....
Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., and Schmid, J. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310.
Pruning Meta-Classifiers in a Distributed Data Mining System - Prodromidis, Stolfo (1998) (7 citations) (Correct)
.... data and compute descriptive representations (also called classifiers or models) Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value [22] Machine learning algorithms have been deployed in heart disease diagnosis [29], in predicting glucose levels for diabetic patients [12] in detecting credit card fraud [30] in steering vehicles driving autonomously on public highways at 70 miles an hour [24] in predicting stock option pricing [23] in computing customizing electronic newspapers[15] etc. Many large ....
R.Detrano, A.Janosi, W.Steinbrunn, M.Pfisterer, J.Schmid, S.Sandhu, K.Guppy, S.Lee, and V.Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Bootstrapping with Noise: An Effective Regularization Technique - Yuval Raviv (1996) (9 citations) (Correct)
....ensemble averaging. The two spiral problem, a highly non linear noise free data, is used to demonstrate these findings. The combination of noisy bootstrap and ensemble averaging is also shown useful for generalized additive modeling, and is also demonstrated on the well known Cleveland Heart Data [7]. Keywords: Noise Injection, Combining Estimators, Pattern Classification, Two Spiral Problem Clinical Data Analysis. 1 Introduction The bootstrap technique has become one of the major tools for producing empirical confidence intervals of estimated parameters or predictors [8] One way to view ....
....we show, that even in this case, training with noise is a very effective approach for smoothing the estimator. In addition to demonstrating our method on a different class of predictors the generalized additive models, we also apply it to another well known data set the Cleveland Heart Data [7]. 2 Theoretical considerations There are a number of factors that have to be applied carefully when trying to regularize an estimator. The regularization is aimed at finding an optimal tradeoff between the variance and bias of the estimator [11] and for best performance, one has to utilize this ....
[Article contains additional citation context not shown here]
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Transferring Previously Learned Back-Propagation Neural Networks.. - Pratt (1993) (16 citations) (Correct)
....j = 1:05; ff = 0:7. The source networks had 26 patterns, and were trained for 100 epochs. 4.1. 5 Heart disease diagnosis for Switzerland patients (Heart VAS, Heart HS) Using a 14 attribute set of diagnosis information, networks were trained for a heart disease diagnosis problem [ Schaffer, 1992, Detrano et al. 1989, Gennari et al. 1989, Aha et al. 1991 ] Input features included the following attributes: age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved (on a stress test) an exercise induced angina ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Non-literal Transfer Among Neural Network Learners - Pratt (1993) (4 citations) (Correct)
....only a single male speaker. 24 hidden units were used, with j = 1:1; ff = 0:75. The target task had 20 patterns. 4. Heart disease diagnosis for Switzerland patients, transfer to Hungary: Using a 14attribute set of diagnosis information, we trained networks on a heart disease diagnosis problem [ Detrano et al. 1989, Gennari et al. 1989 ] We used networks trained on two different source problems, represented by data from a hospital in Hungary. The target problem was to train a network to perform the diagnosis task on Swiss patients. 7 hidden units were used, with j = 1:1; ff = 0:55. The target task had ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Solving Linear Inequalities In A Least Squares Sense - Bramley, Winnicka (1994) (2 citations) (Correct)
....fl varies one third of the variables, while for a 9 or 13 dimensional problem, only one tenth or fourteenth of the variables are being changed. 7. Performance on Test Databases. The linear separability methods have been tested on the Wisconsin Breast Cancer and Cleveland Heart Disease Databases [3]. Here the goal is to provide a linear predictor that can be used to distinguish between benign and malignant tumors in the first data base, and patients at risk or not at risk of heart attack in the second data base. Both data sets are available from the University of California Irvine ....
R. Detrano et al., International application of a new probability algorithm for the diagnosis of coronary artery disease, American Journal of Cardiology, 64 (1989), pp. 304--310.
A Benchmark for Classifier Learning - Zheng (1993) (5 citations) (Correct)
....1 Introduction Considerable advances have been made, in the field of classifier learning from examples, making it one of the most active research areas of machine learning. Many algorithms for this task have been developed and applied to problems from a variety of fields such as medical science [Detrano et al. 1989], biology [Qian and Sejnowski, 1988] linguistics [Sejnowski and Rosenberg, 1987] The task of zeroth order classifier learning from examples is generally in the form: Given: a set of examples, called the training set, in the form of a vector of attribute values and known class for each example. ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher, International application of a new probability algorithm for the diagnosis of coronary artery disease, American Journal of Cardiology, 64, 304-31, 1989.
On the Management of Distributed Learning Agents - Prodromidis (1997) (Correct)
....of the desired attribute for some record whose desired attribute value is unknown. Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value [31] Machine learning algorithms have been deployed in heart disease diagnosis [39], in predicting glucose levels for diabetic patients [17] in detecting credit card fraud [41] in steering vehicles driving autonomously on public highways at 70 miles an hour [33] in predicting stock option pricing [32] in computing customizing electronic newspapers[21] etc. Many large ....
R.Detrano, A.Janosi, W.Steinbrunn, M.Pfisterer, J.Schmid, S.Sandhu, K.Guppy, S.Lee, and V.Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Geometry in Learning - Bennett, Bredensteiner (1997) (1 citation) (Correct)
....a number of patients whose heart disease status is known. By evaluating a new patient s attributes with respect to the separating plane a diagnosis is made. The Cleveland Heart Disease Database (Heart) is a publicly available dataset that contains information on 297 patients using 13 attributes [6]. A second application, as discussed previously, is the diagnosis of breast cancer. To evaluate whether a tumor is benign or malignant, a fine needle aspiration is performed collecting a small amount of tissue from the tumor for examination. Several measurements such as clump thickness, uniformity ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Using the Discriminability Based Transfer Algorithm to Selectively .. - Pratt (2 citations) (Correct)
....only a single male speaker. 24 hidden units were used, with j = 1:1; ff = 0:75. The target task had 20 patterns. 4. Heart disease diagnosis for Switzerland patients, transfer to Hungary: Using a 14 attribute set of diagnosis information, we trained networks on a heart disease diagnosis problem [ Detrano et al. 1989, Gennari et al. 1989 ] We used networks trained on two different source problems, represented by data from a hospital in Hungary. The target problem was to train a network to perform the diagnosis task on Swiss patients. 7 hidden units were used, with j = 1:1; ff = 0:55. The target task had ....
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Unknown - (1993) (Correct)
No context found.
Detrano,R., Janosi,A., Steinbrunn,W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disese. American Hournal of Cardiology, 64, 304-310.
Support Vector Machines for Linear Programming: - Motivation And Formulations (Correct)
No context found.
R. Detrano, A. Janosi, W. Steinbrunn, M. P sterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304-310, 1989.
Brushing Histories to Compare Models - Almond (1994) (Correct)
No context found.
Robert Detrano, Andras Janosi, Walter Steinbrunn, Matthias Pfisterer, Johann-Jakob Schmid, Sarbjit Sandhu, Kern H. Guppy, Stella Lee, and Victor Froelicher [1989]. "International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease." American Jouran of Cardiology, (64) 304--310.
A Parametric Optimization Method for Machine Learning - Bennett, Bredensteiner (1995) (6 citations) (Correct)
No context found.
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, and V. Froelicher. International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64:304--310, 1989.
Multivariate versus Univariate Decision Trees - Brodley, Utgoff (1992) (24 citations) (Correct)
No context found.
Detrano,R., Janosi,A., Steinbrunn,W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disese.
Artificial Neural Networks for the Diagnosis of Coronary.. - Tang, Pingle, Srikant (Correct)
No context found.
Detrano, R. et al. 1989. International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease. American Journal of Cardiology, 64, 304--310.
Robust Linear Programming Discrimination Of Two Linearly.. - Bennett, Mangasarian (1992) (87 citations) (Correct)
No context found.
R. Detrano, A. Janosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, V. Froelicher, International Application of a New Probability Algorithm for the Diagnosis of Coronary Artery Disease, American Journal of Cardiology 64, 1989, pp. 304-310.
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