Comparative analysis of traditional machine learning and automated machine learning: advancing inverted papilloma versus associated squamous cell carcinoma diagnosis.

Journal: International forum of allergy & rhinology
PMID: 39186252

Abstract

Inverted papilloma conversion to squamous cell carcinoma is not always easy to predict. AutoML requires much less technical knowledge and skill to use than traditional ML. AutoML surpassed the traditional ML algorithm in differentiating IP from IP-SCC.

Authors

  • Farideh Hosseinzadeh
    Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
  • S Saeed Mohammadi
    Byers Eye Institute, Department of Ophthalmology, Stanford University, Palo Alto, California, USA.
  • James N Palmer
    Department of Otolaryngology-Head and Neck Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA.
  • Michael A Kohanski
    Department of Otolaryngology-Head and Neck Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA.
  • Nithin D Adappa
    Department of Otolaryngology-Head and Neck Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA.
  • Michael T Chang
    Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.
  • Peter H Hwang
    Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
  • Jayakar V Nayak
    Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA.
  • Zara M Patel
    Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, CA, USA.

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