{'fraction_non_alphanumeric': 0.012674271229404309, 'fraction_numerical': 0.0, 'mean_word_length': 5.4227642276422765, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Integrated Machine Learning Model for Prediction of Lung Cancer Stages from Textual data using Ensemble Method

Research and Development on cancer detection is more on imaging than textual data. With the help of documented symptoms in the form of text and Machine Learning (ML) techniques, it is possible to predict the lung cancerstages effectively. This paper conjectures the oeuvre modelwhich is efficient in predicting the stages of lung carcinoma by applying the concepts of ML algorithms. The proposed model is combination of K-Nearest Neighbours, Decision Tree and Neural Networks modelsalong with bagging ensemble method for enhancing the accuracy of the overall prediction. The predictedresults of the suggested model are showing better accuracy compared to individual algorithms.