Abstract
From time to time, lung cancer has appeared in the category of nearly the most lethal maladies since humankind existed. It is even among the most incessant fatalities and major reasons of mortality among all cancers. Lung cancer cases are significantly growing. In India, there are around 70 thousand instances each year. Because the condition is usually asymptomatic in its early stages, it is practically extremely difficult to identify. As a result, early cancer identification is beneficial for preserving lives. Early discovery can improve a patient’s chances of rehabilitation and recovery. Technology has a critical part in accurately identifying cancers. Based on these findings, several researchers have offered various ways. Several Computer-aided diagnostics (CAD) methodologies and systems have been suggested, developed and created in recent years to handle this problem using computer technology. Also, there are some factors like smoking, taking alcohol, anxiety etc. that also helps us to detect if patient is having cancer or not has also been taken into considerations by many researchers. Taking on an assortment of techniques such as machine learning, Ensemble learning and deep learning approaches and numerous ways based on image processing techniques and text information, those systems contribute to a great extent to ascertain the cancer malignancy degree. We had in view integrating or putting together the Ensemble learning algorithms like Stacking, blinding, Max voting, boosting and XGBoost through this so as to build an advanced method to assess and scrutinize the out-turn. On comparing Blinding ensemble learning technique proves to be the most efficient technique based on the performance metrics like accuracy, F1 score, precision and recall.
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Fatima, F.S., Jaiswal, A., Sachdeva, N. (2022). Lung Cancer Detection Using Ensemble Learning. In: Sugumaran, V., Upadhyay, D., Sharma, S. (eds) Advancements in Interdisciplinary Research. AIR 2022. Communications in Computer and Information Science, vol 1738. Springer, Cham. https://doi.org/10.1007/978-3-031-23724-9_15
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