Exploring the Impact of Machine Learning on Skin Cancer Diagnosis and the Evolution of SNP Analysis in Healthcare

Abstract

This study explores the potential of Machine Learning (ML) algorithms to predict the likelihood of developing melanoma based on an individual's genetic information, specifically Single Nucleotide Polymorphisms (SNPs). A data-processing pipeline was developed to collect, process, and extract relevant features, followed by feature selection to identify unique SNPs associated with melanoma. Various classification algorithms were evaluated, and the impact of different parameters on predictive performance was analyzed. To enhance scalability for large datasets, the pipeline was implemented using Apache Spark. Additionally, the identified SNPs were manually examined and validated against existing research findings. The results contribute to the growing field of ML applications in medical diagnostics and genetic risk assessment. Therefore, this study represents an initial step toward leveraging computational approaches for early melanoma detection, potentially improving patient outcomes through early intervention and personalized treatment strategies.

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