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@article{175599,
        author = {Varshini T and Dinesh S and Gowsick M and Dr. j. Maria Shyla},
        title = {Predicting the Silent Killer: Machine Learning for Early Diagnosis of Pancreatic Cancer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3489-3494},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175599},
        abstract = {Pancreatic cancer is among the most lethal malignancies, primarily due to its asymptomatic nature in early stages and the resulting late diagnosis. Traditional diagnostic methods, though effective at advanced stages, are often invasive, costly, and inadequate for early detection. This research aims to address these challenges by developing a machine learning-based predictive model for the early detection and risk assessment of pancreatic cancer using structured patient data.
The study involves a systematic approach, including data preprocessing, exploratory data analysis (EDA), feature selection, and the implementation of classification algorithms such as Logistic Regression and Decision Tree Classifiers. Model performance is evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrix to ensure reliability and robustness.
The predictive models identify critical risk factors and offer valuable insights to support clinical decision-making. This research highlights the potential of artificial intelligence in healthcare, offering a non-invasive, cost- effective, and scalable solution for early pancreatic cancer detection, ultimately aiming to improve patient outcomes and contribute to the advancement of AI-driven diagnostic tools.},
        keywords = {},
        month = {April},
        }