Strategie di apprendimento automatico per il diabete di tipo 2 Classificazione del diabete
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Strategie di apprendimento automatico per il diabete di tipo 2 Classificazione del diabete

Strategie di apprendimento automatico per il diabete di tipo 2 Classificazione del diabete


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About the Book

L'aumento dei casi di diabete di tipo 2 ha alimentato la ricerca di solidi sistemi diagnostici. L'integrazione dell'apprendimento automatico migliora questi sistemi analizzando diversi set di dati e affrontando le complicazioni associate come l'obesità, le cattive abitudini e l'ipertensione. La diagnosi precoce è fondamentale, date le gravi implicazioni per la salute. Il ML, abbinato all'elaborazione del linguaggio naturale, aiuta nella prognosi, nella diagnosi e nei piani di prevenzione. Utilizzando il dataset PIDD (768 campioni, 16 attributi), questa ricerca si concentra sulla previsione del diabete con un set di caratteristiche ampliato. La pre-elaborazione prevede la normalizzazione, il bilanciamento con SMOTE e i controlli di completezza per migliorare l'accuratezza del modello. Nel complesso, questo studio sottolinea il ruolo fondamentale del ML nel far progredire la comprensione e le capacità predittive del diabete di tipo 2 attraverso metodologie e selezioni meticolose dei set di dati.


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Product Details
  • ISBN-13: 9786206965138
  • Publisher: KS Omniscriptum Publishing
  • Binding: Paperback
  • Language: Italian
  • Returnable: N
  • Weight: 154 gr
  • ISBN-10: 6206965139
  • Publisher Date: 20 Dec 2023
  • Height: 229 mm
  • No of Pages: 64
  • Spine Width: 4 mm
  • Width: 152 mm


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Strategie di apprendimento automatico per il diabete di tipo 2 Classificazione del diabete
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Strategie di apprendimento automatico per il diabete di tipo 2 Classificazione del diabete
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