Death and Morbidity Prediction Using Data Mining in Perforated Peptic Ulcers

Hugo Peixoto, Lara Silva, Soraia Pereira, Tiago Jesus, Vitor Neves Lopes and António Carlos Abelha
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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

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2020, vol. 9, issue 1, 37-49

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:9:y:2020:i:1:p:37-49

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