Improving the classification of multiple disorders with problem decomposition

AbstractDifferential diagnosis of multiple disorders is a challenging problem in clinical medicine. According to the divide-and-conquer principle, this problem can be handled more effectively through decomposing it into a number of simpler sub-problems, each solved separately. We demonstrate the advantages of this approach using abductive network classifiers on the 6-class standard dermatology dataset. Three problem decomposition scenarios are investigated, including class decomposition and two hierarchical approaches based on clinical practice and class separability properties. Two-stage classification schemes based on hierarchical decomposition boost the classification accuracy from 91% for the single-classifier monolithic approach to 99%, matching the theoretical upper limit reported in the literature for the accuracy of classifying the dataset. Such models are also simpler, achieving up to 47% reduction in the number of input variables required, thus reducing the cost and improving the convenience of performing the medical diagnostic tests required. Automatic selection of only relevant inputs by the simpler abductive network models synthesized provides greater insight into the diagnosis problem and the diagnostic value of various disease markers. The problem decomposition approach helps plan more efficient diagnostic tests and provides improved support for the decision-making process. Findings are compared with established guidelines of clinical practice, results of data analysis, and outcomes of previous informatics-based studies on the dataset

Automatic fitting of Gaussian peaks using abductive machine learning

Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 5.8%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum

Enhancement of two-dimensional holographic images by resolutionimprovement through hologram aperture expansion

The application of the 1-D aperture expansion algorithm to 2-D holograms is demonstrated for the cases of 1-point, 2-point, and quasi-continuous objects in the presence of noise. Due to the 1-D nature of the algorithm, different predictive models used for rows and columns in the case of 2-point and similar objects; which calls for some prior knowledge of the object geometry. Quality of images from expanded holograms may be improved by reconstructing at a distance which optimizes a quality criterion in the image. Of the two methods considered for reconstructing area holograms the 2-D method performs better with prediction errors and noise and is computationally faste

Expansion of two-dimensional imaging apertures for resolutionimprovement in long-wavelength holography

A method is described for improving resolution in long-wavelength holography by expanding the two-dimensional hologram aperture. Simulation results are presented to illustrate resolution improvements obtained when doubling the linear size of square apertures for imaging single-point, multiple-point and quasi-continuous objects in the presence of noise. A technique for improving the quality of images reconstructed from predicted holograms using a number of focus measures is described. Alternative strategies for reconstructing the two-dimensional holograms are discussed and their performance compared regarding prediction errors and noise. Data is supplied on CPU time usage for hologram expansion and reconstruction on a VAX-11/785 compute

Automatic fitting of Gaussian peaks using abductive machine learning

Analytical techniques have been used for many years for fitting Gaussian peaks in nuclear spectroscopy. However, the complexity of the approach warrants looking for machine-learning alternatives where intensive computations are required only once (during training), while actual analysis on individual spectra is greatly simplified and quickened. This should allow the use of simple portable systems for fast and automated analysis of large numbers of spectra, particularly in situations where accuracy may be traded for speed and simplicity. This paper proposes the use of abductive networks machine learning for this purpose. The Abductory Induction Mechanism (AIM) tool was used to build models for analyzing both single and double Gaussian peaks in the presence of noise depicting statistical uncertainties in collected spectra. AIM networks were synthesized by training on 1000 representative simulated spectra and evaluated on 500 new spectra. A classifier network determines the multiplicity of single/double peaks with an accuracy of 5.8%. With statistical uncertainties corresponding to a peak count of 100, average percentage absolute errors for the height, position, and width of single peaks are 4.9, 2.9, and 4.2%, respectively. For double peaks, these average errors are within 7.0, 3.1, and 5.9%, respectively. Models have been developed which account for the effect of a linear background on a single peak. Performance is compared with a neural network application and with an analytical curve-fitting routine, and the new technique is applied to actual data of an alpha spectrum

Short-term hourly load forecasting using abductive networks

Short-term load modeling and forecasting are essential for operating power utilities profitably and securely. Modern machine learning approaches, such as neural networks, have been used for this purpose. This paper proposes using the alternative technique of abductive networks, which offers the advantages of simplified and more automated model synthesis and analytical input-output models that automatically select influential inputs, provide better insight and explanations, and allow comparison with statistical and empirical models. Using hourly temperature and load data for five years, 24 dedicated models for forecasting next-day hourly loads have been developed. Evaluated on data for the sixth year, the models give an overall mean absolute percentage error (MAPE) of 2.67%. Next-hour models utilizing available load data up to the forecasting hour give a MAPE of 1.14%, outperforming neural network models for the same utility data. Two methods of accounting for the load growth trend achieve comparable performance. Effects of varying model complexity are investigated and proposals made for further improving forecasting performance

Enhancement of two-dimensional holographic images by resolutionimprovement through hologram aperture expansion

The application of the 1-D aperture expansion algorithm to 2-D holograms is demonstrated for the cases of 1-point, 2-point, and quasi-continuous objects in the presence of noise. Due to the 1-D nature of the algorithm, different predictive models used for rows and columns in the case of 2-point and similar objects; which calls for some prior knowledge of the object geometry. Quality of images from expanded holograms may be improved by reconstructing at a distance which optimizes a quality criterion in the image. Of the two methods considered for reconstructing area holograms the 2-D method performs better with prediction errors and noise and is computationally faste

Short-term hourly load forecasting using abductive networks

Short-term load modeling and forecasting are essential for operating power utilities profitably and securely. Modern machine learning approaches, such as neural networks, have been used for this purpose. This paper proposes using the alternative technique of abductive networks, which offers the advantages of simplified and more automated model synthesis and analytical input-output models that automatically select influential inputs, provide better insight and explanations, and allow comparison with statistical and empirical models. Using hourly temperature and load data for five years, 24 dedicated models for forecasting next-day hourly loads have been developed. Evaluated on data for the sixth year, the models give an overall mean absolute percentage error (MAPE) of 2.67%. Next-hour models utilizing available load data up to the forecasting hour give a MAPE of 1.14%, outperforming neural network models for the same utility data. Two methods of accounting for the load growth trend achieve comparable performance. Effects of varying model complexity are investigated and proposals made for further improving forecasting performance

Performance of the VAX 11/785 data acquisition system at the KFUPMEnergy Research Laboratory

The VAX 785 XSYS data acquisition and analysis system at the Energy Research Laboratory (ERL) of King Fahd University of Petroleum and Minerals has been in use since June 1987. The authors describe features unique to the ERL system and present data on the various aspects of its performance in comparison with other XSYS installations, including data acquisition, sorting, and dumping onto disks and tapes. Results show the effects of data rates and key system parameters on both system deadtime and CPU (central processing unit) time usag

Performance of the VAX 11/785 data acquisition system at the KFUPMEnergy Research Laboratory

The VAX 785 XSYS data acquisition and analysis system at the Energy Research Laboratory (ERL) of King Fahd University of Petroleum and Minerals has been in use since June 1987. The authors describe features unique to the ERL system and present data on the various aspects of its performance in comparison with other XSYS installations, including data acquisition, sorting, and dumping onto disks and tapes. Results show the effects of data rates and key system parameters on both system deadtime and CPU (central processing unit) time usag