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Extensions of Logical Analysis of Data for growth hormone deficiency diagnoses

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  • Pierre Lemaire

Abstract

We propose two extensions of the Logical Analysis of Data (LAD) methodology, designed in the context of diagnosing growth hormone deficiencies. On the one hand, combinatorial regression extends the standard methodology from classification problems to regression problems; it permits to predict the final height of children with particular growth troubles. On the other hand, function-based patterns extend the standard notion of pattern, leading to both accurate and simple models; it allows to produce an efficient diagnosis, straightforwardly usable by a general practitioner, that settles most of the doubtful cases of growth hormone deficiencies among short children. In both cases, we show the interest of the LAD extensions for each application, and we also point out the more general use that can be achieved through the two proposed approaches. Copyright Springer Science+Business Media, LLC 2011

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  • Pierre Lemaire, 2011. "Extensions of Logical Analysis of Data for growth hormone deficiency diagnoses," Annals of Operations Research, Springer, vol. 186(1), pages 199-211, June.
    • Handle: RePEc:spr:annopr:v:186:y:2011:i:1:p:199-211:10.1007/s10479-011-0901-8
    DOI: 10.1007/s10479-011-0901-8
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    References listed on IDEAS

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    1. Peter Hammer & Tibérius Bonates, 2006. "Logical analysis of data—An overview: From combinatorial optimization to medical applications," Annals of Operations Research, Springer, vol. 148(1), pages 203-225, November.
    2. Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
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    Cited by:

    1. Travaughn C. Bain & Juan F. Avila-Herrera & Ersoy Subasi & Munevver Mine Subasi, 2020. "Logical analysis of multiclass data with relaxed patterns," Annals of Operations Research, Springer, vol. 287(1), pages 11-35, April.

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