Machine Learning Approach for Feature Interpretation and Classification of Genetic Mutations Leading to Tumor and Cancer

摘要

As the interpretation of genetic mutation is done manually, it is difficult to diagnose a large number of patients and get reports of the same in a quick time. Hence, it needs to be automated using machine learning approach. Towards the same, natural language processing (NLP) technique, viz. term frequency-inverse document frequency (TF-IDF), is used to represent documents as fixed-size depiction for interpreting the given nine classes of genetic mutations. The main aim of this study is to identify the well-suited machine learning model which will give better results in terms of multi-class log-loss. Another important aspect of this study is to interpret the features since feature interpretability is very important in healthcare domain using various machine learning algorithms. Logistic regression (LR) with class balancing was implemented by taking top 1000 words of 3-gram TF-IDF generated features that outperformed the other classifiers to give a test log-loss of 0.98.