Enhanced Skin Cancer Prediction with Analysis Using Machine Learning Algorithms

Abstract

The article discusses about a number of machine learning techniques that can be used for detecting skin cancer. This research study aims to assist the clinicians to precisely detect skin cancer in a patient and begin therapy at an early stage. Several algorithms, including XG Boost Classifier, Random Forest, and Logistic Regression, CNN and KNN algorithm, were used to train and test the dataset provided by Kaggle.com. This study considered 11 attributes, trained and tested the dataset from the source, and employed multiple machine learning techniques. When compared to other methods, the voting ensemble approach produced 85.02 % accuracy.

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