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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing

Ting-Ya Yang¹, Tsair-Wei Chien², Feng-Jie Lai³

¹Department of Family Medicine, Chi Mei Medical Center, Tainan, Taiwan.

JMIR Medical Informatics

|March 9, 2022

Summary

This study developed a machine learning model for skin cancer (SC) risk assessment using computerized adaptive testing (CAT). The resulting app aids early SC risk detection, improving patient self-assessment and early diagnosis.

Keywords:

Rasch partial credit model computerized adaptive testing k-nearest neighbors logistic regression mobile phone naïve Bayes receiver operating characteristic curve skin cancer assessment

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Area of Science:

Dermatology
Medical Informatics
Machine Learning

Background:

Web-based computerized adaptive testing (CAT) can reduce participant burden for skin cancer (SC) risk assessment without compromising precision.
Previous academic reports on CAT for SC classification are lacking.

Purpose of the Study:

To develop a machine learning-based CAT model for an app to automatically classify SC risk.
To enable early-stage SC risk assessment for patients.

Main Methods:

Utilized data from an Australian cohort study (N=43,794) with a Rasch simulation scheme.
Calibrated 30 items using the Rasch partial credit model and simulated 1000 cases.
Compared naïve Bayes, k-nearest neighbors, and logistic regression models using a 70:30 train-test split, evaluating accuracy, sensitivity, specificity, and AUC.

Main Results:

The 30-item k-nearest neighbors model achieved high AUC values (99% training, 91% testing) with hold-out validation.
K-fold cross-validation showed lower AUC values (85%) for the k-nearest neighbors model.
A functional app for SC risk classification was successfully developed and demonstrated.

Conclusions:

The 30-item SC prediction model integrated with Rasch web-based CAT is recommended for patient SC classification.
The developed app facilitates early self-assessment of SC risk, with future application potential.