Artif Intell Med - A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

Tópicos

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Resumo

JECTIVE: Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis.MATERIALS AND METHODS: The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks.RESULTS: The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619.CONCLUSION: The PEL technique is capable of addressing imbalanced sample learning associated with acute appendicitis diagnosis. Our evaluation results suggest PEL is less biased toward a positive or negative class than the investigated benchmark techniques. In addition, our results indicate the overall effectiveness of the proposed technique, compared with prevalent scoring systems or salient classification techniques that follow the resampling approach.

Resumo Limpo

jectiv acut append common medic condit whose effect time diagnosi can difficult miss diagnosi put patient danger also requir addit resourc correct treatment acut append diagnosi constitut classif problem fundament challeng pertain skew outcom class distribut instanc train sampl preclusteringbas ensembl learn pel techniqu aim address associ imbalanc sampl learn problem therebi support time accur diagnosi acut appendicitismateri method propos pel techniqu employ undersampl reduc number majorityclass instanc train sampl use preclust group similar majorityclass instanc multipl group select group repres instanc creat balanc sampl pel techniqu therebi reduc potenti inform loss random undersampl also take advantag ensembl learn improv perform empir evalu propos techniqu clinic case obtain comprehens tertiari hospit southern taiwan use sever preval techniqu salient score system benchmarksresult compar result show pel effect less bias benchmark propos pel techniqu seem sensit identifi posit acut append common use alvarado score system exhibit higher specif identifi negat acut append addit sensit specif valu pel appear higher investig benchmark follow resampl approach analysi suggest pel benefit repres majorityclass instanc train sampl accord overal evalu result pel record best overal perform area curv measur reach conclus pel techniqu capabl address imbalanc sampl learn associ acut append diagnosi evalu result suggest pel less bias toward posit negat class investig benchmark techniqu addit result indic overal effect propos techniqu compar preval score system salient classif techniqu follow resampl approach

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