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Death and Morbidity Prediction Using Data Mining in Perforated Peptic Ulcers

Author

Listed:
  • Hugo Peixoto

    (Algoritmi Research Center, University of Minho, Braga, Portugal)

  • Lara Silva

    (University of Minho, Braga, Portugal)

  • Soraia Pereira

    (University of Minho, Braga, Portugal)

  • Tiago Jesus

    (University of Minho, Braga, Portugal)

  • Vitor Neves Lopes

    (Centro Hospitalar do Tâmega e Sousa, Guilhufe, Portugal)

  • António Carlos Abelha

    (Algoritmi Research Center, University of Minho, Braga, Portugal)

Abstract

Peptic ulcers are not the most common complication in gastrointestinal mucosa, but these defects stand out as being the complication with the highest mortality rate. Several scoring systems based on clinical and biochemical parameters, such as the Boey and PULP scoring system have been developed to predict the probability of mortality. In this study, a data mining process is performed in the medical data available, in order to evaluate how the scoring systems perform when trying to predict mortality and patients' state complication. Furthermore, the presented paper studies the two scoring systems presented to define which one outperforms the other. On one hand PULP scoring allows a better mortality prediction achieving, above a 90% accuracy. One the other hand, regarding complications, the Boey system achieves better results leading to a better prediction when it comes to predicting patients' state complication.

Suggested Citation

  • Hugo Peixoto & Lara Silva & Soraia Pereira & Tiago Jesus & Vitor Neves Lopes & António Carlos Abelha, 2020. "Death and Morbidity Prediction Using Data Mining in Perforated Peptic Ulcers," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 9(1), pages 37-49, January.
    • Handle: RePEc:igg:jrqeh0:v:9:y:2020:i:1:p:37-49
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