The objective of this paper is to develop a Machine learning model that can classify cancer patients. Gene mutation-based treatment has a very good success ratio, but only a few cancer institutes follow it. This research uses natural language processing techniques to remove unwanted text and convert the categorical data into numerical data using response coded and one-hot encoded. Then we apply various classification algorithms to classify the training data. The proposed system has the advantage of reducing the time to analyze and classify clinical data of patients, which translates into less wait time for patients in order to get results from pathologists. The results of our experiment will demonstrate that the Stacking Classifier algorithm with One-Hot encoding and Term Frequency – Inverse Document Frequency (TF-IDF) techniques perform better than other Machine Learning methods with around 67% accuracy on the test data.      «

The objective of this paper is to develop a Machine learning model that can classify cancer patients. Gene mutation-based treatment has a very good success ratio, but only a few cancer institutes follow it. This research uses natural language processing techniques to remove unwanted text and convert the categorical data into numerical data using response coded and one-hot encoded. Then we apply various classification algorithms to classify the training data. The proposed system has the advantage...     »