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Leveraging Explainable AI for Early Risk Prediction and Type Classification for Leukemia: Insights Using Clinical

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    Lifestyle factors like passive smoking, rural living, and poor nutrition increase leukemia risk in Pakistan. Promoting healthier choices may reduce childhood cancer incidence.

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    Area of Science:

    • Hematology and Oncology
    • Public Health and Epidemiology
    • Bioinformatics and Computational Biology

    Background:

    • Leukemia is a significant childhood cancer, with limited research on lifestyle and demographic risk factors specific to Pakistan.
    • Understanding regional lifestyle variations is crucial for effective leukemia risk mitigation strategies.

    Purpose of the Study:

    • To identify lifestyle and demographic factors linked to leukemia development in Pakistan.
    • To predict specific leukemia subtypes using clinical data and machine learning.
    • To address a research gap in comprehensive leukemia subtype analysis within the Pakistani population.

    Main Methods:

    • Collected data from 364 leukemia cases and 896 controls in Islamabad and Peshawar, Pakistan.
    • Utilized Machine Learning, statistical, and graph-based methods for risk factor analysis and leukemia classification.
    • Employed SHapley Additive exPlanations (SHAP) for interpretability of classification outcomes.

    Main Results:

    • Identified passive smoking, rural residence, and poor nutrition as significant risk factors for leukemia.
    • Achieved 96% accuracy in leukemia classification using structured (tabular) data, particularly with oversampled data.
    • Successfully transformed clinical data into graph data for leukemia classification and subtype prediction.

    Conclusions:

    • Promoting healthier lifestyle choices is essential for potentially reducing leukemia incidence in Pakistan.
    • Machine learning models demonstrate high accuracy in leukemia classification and subtype prediction.
    • SHAP analysis provides valuable insights into the factors driving leukemia classification.