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- W4382117970 abstract "Cancer is the leading consequence of death around the world, accounting for more than ten million deaths in recent times. Lung cancer is the second most common type of cancer. The stage of cancer is determined by the size of the tumour and how quickly it spread. Recent breakthroughs in imaging and sequencing technology have made it possible for researchers to make methodical progress in their clinical studies of lung cancer. Approaches that are based on machine learning play an important part in the process of involving and evaluating big and complicated datasets. These datasets have comprehensively described lung cancer by using a variety of viewpoints that are derived from the accumulated data. The results suggest that our proposed approach outperforms other ML algorithms. The proposed model outperforms other machine learning techniques such as decision tree, SVM, KNN, random forest, XGBoost which are used to do comparison analysis, and their accuracy, precision, recall, and F1-score are determined. In our research, we found that our proposed method outperforms other machine learning models. According to the findings of our investigation, the approach that we have presented, ensemble model, performs better than other machine learning models. We were able to get an accuracy of 97.01%, a precision of 99.21%, a recall of 94.7%, and F1-score percentage of 96.9%." @default.
- W4382117970 created "2023-06-27" @default.
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- W4382117970 date "2023-01-01" @default.
- W4382117970 modified "2023-09-24" @default.
- W4382117970 title "An Approach Towards Early Stage Detection of Lung Cancer Using Machine Learning" @default.
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- W4382117970 doi "https://doi.org/10.1007/978-981-99-1699-3_37" @default.
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