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- W4313444877 abstract "Lung cancer is one of the most common deaths causing cancers in the world. The late diagnosis would lead to the rapid spread of cancer to other organs in the body and becomes life threatening. Studies say that the five-year survival rate of late diagnosed lung cancer cases are only 4%, and only about 16% of the cases are diagnosed at an early stage. The major cause that paves for the late diagnosis of lung cancer is that the initial clinical symptoms are often neglected as inconsequential. The widely used method for lung cancer diagnosis is radiomics. However, radiomics is expensive and the recurrent subjection to it itself can be risky. Also, in most of the cases, it is found to be performed in a late stage. Use of clinical data is a possible alternative for radiomics in early diagnosis of lung cancer. In this work, we tried to identify the predictors from clinical lung cancer data using machine learning (ML) techniques. Through the secondary data analysis, we identified the most influential predictors of lung cancer from the clinical data. These prognostic markers can aid in the early detection of lung cancer. The patients that hit the specified marker values can be classified as potential risky group to develop lung cancer. Initially, in our approach, the data is pre-processed and feature selection is applied using data filtering method. Then, we applied ML models such as support vector machine (SVM) and random forest (RF) to classify the patients." @default.
- W4313444877 created "2023-01-06" @default.
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- W4313444877 date "2023-01-01" @default.
- W4313444877 modified "2023-10-16" @default.
- W4313444877 title "Identifying the Predictors from Lung Cancer Data Using Machine Learning" @default.
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- W4313444877 doi "https://doi.org/10.1007/978-981-19-5443-6_53" @default.
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