Abstract

The use of artificial intelligence (AI) in medicine requires a careful selection of suitable models, as there is no universal “one size fits all” method. While linear regression is convincing due to its simplicity and interpretability, it is limited due to the assumption of linearity and susceptibility to multicollinearity and outliers. More complex approaches such as neural networks show their strengths in the detection of non-linear patterns and automatic feature extraction but require large amounts of data, high computing capacity, and suffer from limited explainability. Principal component analysis (PCA) offers an efficient reduction of dimensionality. Ultimately, the choice of model depends on the balance between accuracy, interpretability, and data availability. A selection of machine learning models is presented in this article.

Author Details

Authors

• Stephan Traidl¹
• Sonja Mathes²
• Sebastian Seurig³

Departments

• ¹ Department of Dermatology and Allergy, Hannover Medical School, Hanover,
• ² Department of Dermatology and Allergy, School of Medicine and Health, Technical University of Munich, Munich, and
• ³ Department of Respiratory Medicine, Allergology and Sleep Medicine, General Hospital Nuremberg, Campus North, Paracelsus Medical University, Nuremberg, Germany

Address

PD Dr. med. Stephan Traidl, MSc, Department of Dermatology and Allergy, Hannover Medical School, Carl-Neuberg-Str.1, 30625 Hanover, Germany
Email: [email protected]

Citation