Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction

  • Maria PedrotoEmail author
  • Alípio Jorge
  • João Mendes-Moreira
  • Teresa Coelho
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 803)


Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient’s attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.


Genealogical data Regression algorithms Relevancy estimation Feature construction 



This work is financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013 and by Centro Hospitalar do Porto (ChPorto) through grant BI.09/2015/UCA/CHP.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Maria Pedroto
    • 1
    • 2
    Email author
  • Alípio Jorge
    • 1
  • João Mendes-Moreira
    • 1
  • Teresa Coelho
    • 2
  1. 1.1-LIAAD/INESC TEC, University of PortoPortoPortugal
  2. 2.Unidade Corino de Andrade (UCA), Centro Hospitalar do Porto (CHP)PortoPortugal

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