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Symptom Analysis using a Machine Learning approach for Early Stage Lung Cancer
Published in Institute of Electrical and Electronics Engineers Inc.
Pages: 246 - 250
The integration of the machine learning techniques in healthcare can be of huge benefit aimed at curing illness of millions of people. A lot of effort has been taken by researchers to detect and provide early-stage insights into cancer diagnosis. In the machine learning research community, various algorithms-KNN, SVM, Decision Trees and Random Forest have been applied to calculate the presence or decisiveness of cancer in correspondence with the symptoms shown by the patients. This paper aims to analyze the symptoms of the different age groups Youth, Working Class and Elderly. Tree-based algorithms like Decision Trees, Random Forest and XGBoost have been used to identify the underlying data patterns in order to calculate relative feature importances. It has been concluded that Coughing of Blood, Clubbing of Finger Nails, Genetic Risk, Passive Smoking and Snoring are the factors that are responsible for lung cancer in all the age groups in most of the cases. © 2020 IEEE.