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Un modello random forest per la classificazione del cancro al seno

Un modello random forest per la classificazione del cancro al seno


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About the Book

Questo libro evidenzia lo sviluppo e l'ottimizzazione di un modello Random Forest (RF) per classificare il cancro al seno come benigno o maligno utilizzando il Wisconsin Breast Cancer Dataset. Dopo la preelaborazione di 569 campioni (357 benigni, 212 maligni), un modello RF predefinito ha raggiunto un'accuratezza del 95,61%. Per migliorare i risultati, è stata applicata la regolazione degli iperparametri tramite Grid Search, regolando parametri quali il numero di alberi (150), la profondità massima (Nessuno), la divisione minima dei campioni (2), la foglia minima dei campioni (1) e il seme casuale (123). Il modello RF ottimizzato ha raggiunto il 99,12% di accuratezza, precisione, richiamo e punteggio F1, superando altri metodi come SVM, XGBoost e precedenti implementazioni di RF. I risultati mostrano una riduzione dei falsi negativi e l'assenza di falsi positivi, indicando un'elevata sensibilità e specificità. Il lavoro sottolinea il valore di una meticolosa regolazione degli iperparametri nelle applicazioni di IA medica e suggerisce una futura integrazione con reti neurali e modelli ibridi per migliorare le prestazioni nella diagnosi clinica del cancro al seno.


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Product Details
  • ISBN-13: 9786209051173
  • Publisher: Edizioni Sapienza
  • Publisher Imprint: Edizioni Sapienza
  • Height: 229 mm
  • No of Pages: 64
  • Returnable: N
  • Weight: 150 gr
  • ISBN-10: 6209051170
  • Publisher Date: 26 Sep 2025
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Spine Width: 4 mm
  • Width: 152 mm


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Un modello random forest per la classificazione del cancro al seno
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