Analyzing Behavior of Cancer Patients using Machine Learning Techniques
Jaswinder Singh1, Sandeep Sharma2

1Jaswinder Singh, Department of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India.
2Sandeep Sharma, Department of Computer Science & Engineering, Guru Nanak Dev University, Amritsar, Punjab, India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1547-1556| Volume-8 Issue-9, July 2019 | Retrieval Number: I8414078919/19©BEIESP | DOI: 10.35940/ijitee.I8414.078919
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Abstract: The online discussion forums and blogs are very vibrant platforms for cancer patients to express their views in the form of stories. These stories sometimes become a source of inspiration for some patients who are anxious in searching the similar cases. This paper proposes a method using natural language processing and machine learning to analyze unstructured texts accumulated from patient’s reviews and stories. The proposed methodology aims to identify behavior, emotions, side-effects, decisions and demographics associated with the cancer victims. The pre-processing phase of our work involves extraction of web text followed by text-cleaning where some special characters and symbols are omitted, and finally tagging the texts using NLTK’s (Natural Language Toolkit) POS (Parts of Speech) Tagger. The post-processing phase performs training of seven machine learning classifiers (refer Table 6). The Decision Tree classifier shows the higher precision (0.83) among the other classifiers while, the Area under the operating Characteristics (AUC) for Support Vector Machine (SVM) classifier is highest (0.98).
Keywords: Cancer Patients, Decision Tree, Feature Extraction, Machine Learning, Natural Language Processing.

Scope of the Article: Predictive Analysis