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@article{168093,
        author = {Bushra Fatima Khan and Shifa Ansari and Maimuna Ahmed},
        title = {Lung Cancer Prediction using Random Forest and Support Vector Machine Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {1514-1519},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168093},
        abstract = {Lung cancer is a major global health concern, accounting for a significant proportion of cancer-related deaths worldwide. It is characterized by the uncontrolled growth of abnormal cells in the lungs, which can spread to other parts of the body if not detected and treated early. Therefore, early diagnosis of lung cancer is critical for improving patient outcomes and survival rates. Machine learning techniques have shown significant promise in disease prediction by identifying lifestyle patterns and existing health concerns. In this paper, we deployed predictive machine learning models like Random Forest (RF) and Support Vector Machines (SVM) for early diagnosis of lung cancer using survey lung cancer dataset. Additionally, we also built data visualizations to identify patterns and utilized evaluation metrics like precision, recall, f-1 score and support to check accuracy our models.},
        keywords = {Lung Cancer, Machine Learning, Random Forest, Support Vector Machine},
        month = {October},
        }